{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e68502a2",
   "metadata": {},
   "source": [
    "# 0. Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b29e0256",
   "metadata": {},
   "source": [
    "!pip install pandas\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86f34158",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "from scipy.io import savemat\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8fc4521",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(0)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a011e9b",
   "metadata": {},
   "source": [
    "# 1. Define Something in Advance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7b98628",
   "metadata": {},
   "outputs": [],
   "source": [
    "def U(x):\n",
    "    return np.exp(-x)*np.sin(m*np.pi*x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52694299",
   "metadata": {},
   "source": [
    "# 2. Initialize the Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb09ad87",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_domain = np.linspace(0, np.pi, 200)\n",
    "delta = x_domain[1] - x_domain[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff173b7",
   "metadata": {},
   "source": [
    "# 3. Define Physical Info."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a38f16",
   "metadata": {},
   "source": [
    "    suppose: x = x[0]; u_x = x[1]; dudx = x[2]; du2dx = x[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f286f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_components = []\n",
    "governing_equation_components.append(Lambda(lambda x: l*x[2]))\n",
    "governing_equation_components.append(Lambda(lambda x: g*tf.sin(x[2])))\n",
    "governing_equation_components.append(Lambda(lambda x: x[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ed72081",
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_mask = x_domain*0 + 1\n",
    "governing_equation_mask[0] = 0\n",
    "governing_equation_mask[-1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bed8ff59",
   "metadata": {},
   "outputs": [],
   "source": [
    "l = 1\n",
    "g = 1\n",
    "m = 2\n",
    "fx = governing_equation_mask*(\n",
    "           l*(2*m*np.pi*x_domain*np.exp(-x_domain)*np.cos(m*np.pi*x_domain**2)-np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)) + \n",
    "           g*np.sin(2*m*np.pi*x_domain*np.exp(-x_domain)*np.cos(m*np.pi*x_domain**2)-np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)) + \n",
    "           np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e65ebff",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_equation_form = False\n",
    "equation_component_combination = [1.0,1.0,1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a647c8f2",
   "metadata": {},
   "source": [
    "# 4. Define the Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee89c021",
   "metadata": {},
   "source": [
    "    suppose: x = x[0]; u_x = x[1]; dudx = x[2]; du2dx = x[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28d1ac32",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_components = []\n",
    "observation_components.append(Lambda(lambda x: x[1]))\n",
    "observation_components.append(Lambda(lambda x: x[2]))\n",
    "observation_components.append(Lambda(lambda x: x[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41dc785c",
   "metadata": {},
   "source": [
    "    format: [x,combination,value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b0cc96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data = []\n",
    "observation_data.append([0,[1,0,0],U(0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2270ab5c",
   "metadata": {},
   "source": [
    "# 5. Define PICN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50428a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[-1.0/(delta)]],[[1.0/(delta)]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d99b8e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[1.0/(delta**2)]],[[-2.0/(delta**2)]],[[1.0/(delta**2)]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "858a79b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [keras.layers.Input(shape=(1,1)),keras.layers.Input(shape=(len(x_domain),1))]\n",
    "\n",
    "hidden_field = keras.layers.Conv1DTranspose(filters=1, \n",
    "                                            kernel_size=len(x_domain)+4, \n",
    "                                            activation='linear')(inputs[0])\n",
    "coordinates = inputs[1]\n",
    "field = keras.layers.Conv1D(filters=1, \n",
    "                            kernel_size=3, \n",
    "                            padding='valid', \n",
    "                            activation='linear')(hidden_field)\n",
    "gradient_field = keras.layers.Conv1D(filters=1, \n",
    "                                     kernel_size=2, \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_kernal_init)(field)\n",
    "gradient_2_field = keras.layers.Conv1D(filters=1, \n",
    "                                       kernel_size=3, \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_2_kernal_init)(field)\n",
    "phycial_fields = [coordinates,field[:,1:-1,:],gradient_field[:,1:,:],gradient_2_field]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9985fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "if estimate_equation_form==True:\n",
    "    tf_governing_equation_components = [component(phycial_fields) for component in governing_equation_components]\n",
    "    concat_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_governing_equation_components)\n",
    "    governing_equation = keras.layers.Conv1D(filters=1, \n",
    "                                             kernel_size=1, \n",
    "                                             padding='valid',\n",
    "                                             use_bias=False)(concat_equation_components)\n",
    "else:\n",
    "    tf_weighted_governing_equation_components = [weight*component(phycial_fields) for [weight,component] in zip(equation_component_combination,governing_equation_components)]\n",
    "    concat_weighted_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_weighted_governing_equation_components)\n",
    "    governing_equation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_equation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79aaf2f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_observation_components = [component(phycial_fields) for component in observation_components]\n",
    "observation_list = []\n",
    "for data in observation_data:\n",
    "    if data[0] <= x_domain[0]:\n",
    "        left_x_position_index = 0\n",
    "        right_x_position_index = 1\n",
    "        left_x_position_weight = 1\n",
    "        right_x_position_weight = 0\n",
    "    elif data[0] >= x_domain[-1]:\n",
    "        left_x_position_index = -2\n",
    "        right_x_position_index = -1\n",
    "        left_x_position_weight = 0\n",
    "        right_x_position_weight = 1\n",
    "    else:\n",
    "        left_x_position_index = int(np.round((data[0] - x_domain[0])/delta))\n",
    "        right_x_position_index = int(np.round(left_x_position_index + 1))\n",
    "        left_x_position_weight = 1-(data[0] - (x_domain[0]+delta*left_x_position_index))/delta\n",
    "        right_x_position_weight = 1-left_x_position_weight\n",
    "        \n",
    "    left_position_observation_components = [component[:,left_x_position_index,:] for component in tf_observation_components]\n",
    "    right_position_observation_components = [component[:,right_x_position_index,:] for component in tf_observation_components]\n",
    "    \n",
    "    concat_weighted_left_position_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))([weight*component for [weight,component] in zip(data[1],left_position_observation_components)])\n",
    "    concat_weighted_right_position_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))([weight*component for [weight,component] in zip(data[1],right_position_observation_components)])\n",
    "    \n",
    "    left_observation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_left_position_observation_components)\n",
    "    right_observation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_right_position_observation_components)\n",
    "    \n",
    "    observation_list.append(left_x_position_weight*left_observation + right_x_position_weight*right_observation)\n",
    "    \n",
    "observations = Lambda(lambda x: tf.concat(x,axis=-1))(observation_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2c9be15",
   "metadata": {},
   "outputs": [],
   "source": [
    "pde_model = keras.Model(inputs=inputs, outputs=phycial_fields)\n",
    "pde_model_train = keras.Model(inputs=inputs, outputs=[governing_equation,observations])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ea9c324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'input_1:0' shape=(None, 1, 1) dtype=float32>,\n",
       " <tf.Tensor 'input_2:0' shape=(None, 200, 1) dtype=float32>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pde_model_train.input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e102d5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'lambda_7/Sum:0' shape=(None, 200, 1) dtype=float32>,\n",
       " <tf.Tensor 'lambda_12/concat/concat:0' shape=(None, 1) dtype=float32>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pde_model_train.output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dbf6c1e",
   "metadata": {},
   "source": [
    "# 6. Prepare the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e13964ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "unit_constant = np.asarray([[[1.0]]],dtype=np.float32)\n",
    "training_input_data_0 = np.repeat(unit_constant, 1, axis=0)\n",
    "print(training_input_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03abae6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_1 = np.expand_dims(x_domain.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83251413",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_0 = np.expand_dims(fx,axis=(0,-1))\n",
    "print(training_label_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09992122",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_1 = np.expand_dims(np.asarray([data[2] for data in observation_data]),axis=[0])\n",
    "print(training_label_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ae901b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_input_data = [training_input_data_0,training_input_data_1]\n",
    "training_label_data = [training_label_data_0,training_label_data_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3ecd9a",
   "metadata": {},
   "source": [
    "# 6. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da87972b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'lambda_7_loss', 'lambda_12_loss']\n"
     ]
    }
   ],
   "source": [
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(), loss=\"mse\")\n",
    "pde_model_train.save_weights('picn_initial_weights.h5')\n",
    "temp_history = pde_model_train.fit(x=training_input_data, y=training_label_data, epochs=1, verbose=0)\n",
    "history_keys = []\n",
    "for key in temp_history.history.keys():\n",
    "    history_keys.append(key)\n",
    "print(history_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "daa7785a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_predictions():\n",
    "    [_,ux, dudx, d2udx2] = pde_model.predict(training_input_data)\n",
    "    ux_list.append(ux[0,:,0])\n",
    "    dudx_list.append(dudx[0,:,0])\n",
    "    d2udx2_list.append(d2udx2[0,:,0])\n",
    "\n",
    "class Per_X_Epoch_Record(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, record_interval, verbose=1):\n",
    "        super(tf.keras.callbacks.Callback, self).__init__()\n",
    "        self.total_loss = history_keys[0]\n",
    "        self.domain_loss = history_keys[1]\n",
    "        self.bdc_loss = history_keys[2]\n",
    "        self.previous_total_loss = 9999999\n",
    "        self.record_interval = record_interval;\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        if epoch%self.record_interval == 0:\n",
    "            \n",
    "            current_total_loss = logs.get(self.total_loss)\n",
    "            current_domain_loss = logs.get(self.domain_loss)\n",
    "            current_bdc_loss = logs.get(self.bdc_loss)\n",
    "            \n",
    "            epoch_number_list.append(epoch)\n",
    "            total_loss_list.append(current_total_loss)\n",
    "            domain_loss_list.append(current_domain_loss)\n",
    "            boundary_loss_list.append(current_bdc_loss)\n",
    "        \n",
    "            if current_total_loss < self.previous_total_loss:\n",
    "                self.previous_total_loss = current_total_loss\n",
    "                pde_model_train.save_weights('picn_best_weights.h5')\n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                print(\"epoch: {:10.5f} | total_loss: {:10.5f} | domain_loss: {:10.5f} | bdc_loss: {:10.5f}\".format(epoch,current_total_loss,current_domain_loss,current_bdc_loss))\n",
    "        \n",
    "            # evaluate the errors in f-domain\n",
    "            record_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2dfa38e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    Per_X_Epoch_Record(record_interval=200,verbose=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "09eccbbe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:    0.00000 | total_loss:    5.63138 | domain_loss:   56.31379 | bdc_loss:    0.00000\n",
      "epoch:  200.00000 | total_loss:    0.87008 | domain_loss:    8.69920 | bdc_loss:    0.00017\n",
      "epoch:  400.00000 | total_loss:    0.76379 | domain_loss:    7.63684 | bdc_loss:    0.00012\n",
      "epoch:  600.00000 | total_loss:    0.72134 | domain_loss:    7.21229 | bdc_loss:    0.00012\n",
      "epoch:  800.00000 | total_loss:    0.68664 | domain_loss:    6.86566 | bdc_loss:    0.00009\n",
      "epoch: 1000.00000 | total_loss:    0.65697 | domain_loss:    6.56917 | bdc_loss:    0.00006\n",
      "epoch: 1200.00000 | total_loss:    0.64388 | domain_loss:    6.43828 | bdc_loss:    0.00006\n",
      "epoch: 1400.00000 | total_loss:    0.60101 | domain_loss:    6.00938 | bdc_loss:    0.00008\n",
      "epoch: 1600.00000 | total_loss:    0.53931 | domain_loss:    5.39174 | bdc_loss:    0.00015\n",
      "epoch: 1800.00000 | total_loss:    0.53320 | domain_loss:    5.33058 | bdc_loss:    0.00015\n",
      "epoch: 2000.00000 | total_loss:    0.52929 | domain_loss:    5.29160 | bdc_loss:    0.00014\n",
      "epoch: 2200.00000 | total_loss:    0.48720 | domain_loss:    4.87119 | bdc_loss:    0.00009\n",
      "epoch: 2400.00000 | total_loss:    0.48501 | domain_loss:    4.84925 | bdc_loss:    0.00009\n",
      "epoch: 2600.00000 | total_loss:    0.48350 | domain_loss:    4.83431 | bdc_loss:    0.00008\n",
      "epoch: 2800.00000 | total_loss:    0.46376 | domain_loss:    4.63727 | bdc_loss:    0.00004\n",
      "epoch: 3000.00000 | total_loss:    0.46103 | domain_loss:    4.60995 | bdc_loss:    0.00004\n",
      "epoch: 3200.00000 | total_loss:    0.44655 | domain_loss:    4.46536 | bdc_loss:    0.00002\n",
      "epoch: 3400.00000 | total_loss:    0.17462 | domain_loss:    1.74615 | bdc_loss:    0.00001\n",
      "epoch: 3600.00000 | total_loss:    0.10512 | domain_loss:    1.05117 | bdc_loss:    0.00000\n",
      "epoch: 3800.00000 | total_loss:    0.10364 | domain_loss:    1.03633 | bdc_loss:    0.00000\n",
      "epoch: 4000.00000 | total_loss:    0.10298 | domain_loss:    1.02975 | bdc_loss:    0.00001\n",
      "epoch: 4200.00000 | total_loss:    0.10249 | domain_loss:    1.02484 | bdc_loss:    0.00001\n",
      "epoch: 4400.00000 | total_loss:    0.08056 | domain_loss:    0.80536 | bdc_loss:    0.00002\n",
      "epoch: 4600.00000 | total_loss:    0.07989 | domain_loss:    0.79874 | bdc_loss:    0.00002\n",
      "epoch: 4800.00000 | total_loss:    0.07923 | domain_loss:    0.79216 | bdc_loss:    0.00001\n",
      "epoch: 5000.00000 | total_loss:    0.05958 | domain_loss:    0.59584 | bdc_loss:    0.00000\n",
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      "epoch: 9600.00000 | total_loss:    0.01807 | domain_loss:    0.18068 | bdc_loss:    0.00000\n",
      "epoch: 9800.00000 | total_loss:    0.01806 | domain_loss:    0.18057 | bdc_loss:    0.00000\n",
      "epoch: 10000.00000 | total_loss:    0.01806 | domain_loss:    0.18062 | bdc_loss:    0.00000\n",
      "epoch: 10200.00000 | total_loss:    0.01798 | domain_loss:    0.17981 | bdc_loss:    0.00000\n",
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      "epoch: 11600.00000 | total_loss:    0.01797 | domain_loss:    0.17970 | bdc_loss:    0.00000\n",
      "epoch: 11800.00000 | total_loss:    0.01796 | domain_loss:    0.17959 | bdc_loss:    0.00000\n",
      "epoch: 12000.00000 | total_loss:    0.01797 | domain_loss:    0.17969 | bdc_loss:    0.00000\n",
      "epoch: 12200.00000 | total_loss:    0.01798 | domain_loss:    0.17974 | bdc_loss:    0.00000\n",
      "epoch: 12400.00000 | total_loss:    0.01797 | domain_loss:    0.17965 | bdc_loss:    0.00000\n",
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      "epoch: 13000.00000 | total_loss:    0.01796 | domain_loss:    0.17962 | bdc_loss:    0.00000\n",
      "epoch: 13200.00000 | total_loss:    0.01796 | domain_loss:    0.17958 | bdc_loss:    0.00000\n",
      "epoch: 13400.00000 | total_loss:    0.01797 | domain_loss:    0.17967 | bdc_loss:    0.00000\n",
      "epoch: 13600.00000 | total_loss:    0.01796 | domain_loss:    0.17961 | bdc_loss:    0.00000\n",
      "epoch: 13800.00000 | total_loss:    0.01796 | domain_loss:    0.17954 | bdc_loss:    0.00000\n",
      "epoch: 14000.00000 | total_loss:    0.01796 | domain_loss:    0.17956 | bdc_loss:    0.00000\n",
      "epoch: 14200.00000 | total_loss:    0.01796 | domain_loss:    0.17953 | bdc_loss:    0.00000\n",
      "epoch: 14400.00000 | total_loss:    0.01796 | domain_loss:    0.17960 | bdc_loss:    0.00000\n",
      "epoch: 14600.00000 | total_loss:    0.01796 | domain_loss:    0.17955 | bdc_loss:    0.00000\n",
      "epoch: 14800.00000 | total_loss:    0.01796 | domain_loss:    0.17957 | bdc_loss:    0.00000\n",
      "epoch: 15000.00000 | total_loss:    0.01797 | domain_loss:    0.17963 | bdc_loss:    0.00000\n",
      "epoch: 15200.00000 | total_loss:    0.01797 | domain_loss:    0.17962 | bdc_loss:    0.00000\n",
      "epoch: 15400.00000 | total_loss:    0.01796 | domain_loss:    0.17955 | bdc_loss:    0.00000\n",
      "epoch: 15600.00000 | total_loss:    0.00022 | domain_loss:    0.00221 | bdc_loss:    0.00000\n",
      "epoch: 15800.00000 | total_loss:    0.00018 | domain_loss:    0.00185 | bdc_loss:    0.00000\n",
      "epoch: 16000.00000 | total_loss:    0.00018 | domain_loss:    0.00178 | bdc_loss:    0.00000\n",
      "epoch: 16200.00000 | total_loss:    0.00012 | domain_loss:    0.00120 | bdc_loss:    0.00000\n",
      "epoch: 16400.00000 | total_loss:    0.00012 | domain_loss:    0.00116 | bdc_loss:    0.00000\n",
      "epoch: 16600.00000 | total_loss:    0.00011 | domain_loss:    0.00114 | bdc_loss:    0.00000\n",
      "epoch: 16800.00000 | total_loss:    0.00012 | domain_loss:    0.00116 | bdc_loss:    0.00000\n",
      "epoch: 17000.00000 | total_loss:    0.00011 | domain_loss:    0.00115 | bdc_loss:    0.00000\n",
      "epoch: 17200.00000 | total_loss:    0.00010 | domain_loss:    0.00100 | bdc_loss:    0.00000\n",
      "epoch: 17400.00000 | total_loss:    0.00010 | domain_loss:    0.00099 | bdc_loss:    0.00000\n",
      "epoch: 17600.00000 | total_loss:    0.00010 | domain_loss:    0.00097 | bdc_loss:    0.00000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 17800.00000 | total_loss:    0.00011 | domain_loss:    0.00108 | bdc_loss:    0.00000\n",
      "epoch: 18000.00000 | total_loss:    0.00007 | domain_loss:    0.00074 | bdc_loss:    0.00000\n",
      "epoch: 18200.00000 | total_loss:    0.00008 | domain_loss:    0.00078 | bdc_loss:    0.00000\n",
      "epoch: 18400.00000 | total_loss:    0.00008 | domain_loss:    0.00076 | bdc_loss:    0.00000\n",
      "epoch: 18600.00000 | total_loss:    0.00007 | domain_loss:    0.00071 | bdc_loss:    0.00000\n",
      "epoch: 18800.00000 | total_loss:    0.00007 | domain_loss:    0.00073 | bdc_loss:    0.00000\n",
      "epoch: 19000.00000 | total_loss:    0.00007 | domain_loss:    0.00071 | bdc_loss:    0.00000\n",
      "epoch: 19200.00000 | total_loss:    0.00007 | domain_loss:    0.00069 | bdc_loss:    0.00000\n",
      "epoch: 19400.00000 | total_loss:    0.00009 | domain_loss:    0.00086 | bdc_loss:    0.00000\n",
      "epoch: 19600.00000 | total_loss:    0.00007 | domain_loss:    0.00071 | bdc_loss:    0.00000\n",
      "epoch: 19800.00000 | total_loss:    0.00007 | domain_loss:    0.00071 | bdc_loss:    0.00000\n"
     ]
    }
   ],
   "source": [
    "epoch_number_list = []\n",
    "total_loss_list = []\n",
    "domain_loss_list = []\n",
    "boundary_loss_list = []\n",
    "ux_list = []\n",
    "dudx_list = []\n",
    "d2udx2_list = []\n",
    "pde_model_train.load_weights('picn_initial_weights.h5')\n",
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\", loss_weights = [0.1, 0.9])\n",
    "pde_model_train.fit(x=training_input_data, \n",
    "                    y=training_label_data, \n",
    "                    epochs=20000, verbose=0,\n",
    "                    callbacks=callbacks)\n",
    "pde_model_train.load_weights('picn_best_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "983f2f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame({'epoch': epoch_number_list, \n",
    "                 'total_loss': total_loss_list, \n",
    "                 'governing_loss': domain_loss_list, \n",
    "                 'boundary_loss': boundary_loss_list})\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot( 'epoch', 'total_loss', data=df, marker='o', markerfacecolor='red', markersize=2, color='skyblue', linewidth=3)\n",
    "plt.plot( 'epoch', 'governing_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')\n",
    "plt.plot( 'epoch', 'boundary_loss', data=df, marker='', color='blue', linewidth=2, linestyle='dashed')\n",
    "plt.legend()\n",
    "#plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e0a3249",
   "metadata": {},
   "outputs": [],
   "source": [
    "[_, u_pred, dudx_pred, d2udx2_pred] = pde_model.predict(training_input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "984e8c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6psN4T57Gv1zO7vLhR0+ewOHxYNGV4mpACIGh2QguLMHXm4iwpb0WB7gCrhv+SAJTwTgAYDQQPe1zjGH0prOzE8PDw5iamtI7FNVxOBzo7Ows6T66JuBEZAbwTQBXAxgG8DwR3SuEeDlvs2sBbMj+XADg29nfcQBXCCFCRGQF8AQR3S+EeEbTf4KpSn65ewixZAbvubD3tNu8TiuEAIKxJHwum/bBMYpxaCwIANje6av4sXZ01wGQRtqvpAR8NpxAOJFGd9YFqFi2tNfih0/0I5HKwGbhxVStOTa54NowFohic1utjtEwzOlYrVasWbNG7zAMi96fmucDOCaE6BNCJADcAeD6U7a5HsBPhMQzAHxE1Ja9LH8CWbM/K3udg1GEdEbgJ08P4LzeOmxpP/1Ly+uUKknciFn9HJuSPiLWN9dU/FiddU40uG3Yu8L8rwdmIwCAnobSEvCt7V4k0+KkRJDRjiMTC8/7qD+mYyQMw5SD3gl4B4ChvMvD2euK2oaIzES0F8AkgAeEEM+qFyqzUnj0yCQGZyO48RW9BW/3cQK+Yjg2GUKzx547qaoEIsLZXT68MDinQGTGYSibgJdaAd+aPXllHbg+HJ0Mwmk1w2wijAWieofDMEyJ6J2AFxLYnlrFXnQbIURaCHE2gE4A5xPRtoI7IbqJiHYR0a7VoEVilubHTw2gpdaO12xtLXi7rKXkBLz6OToZUqT6LXNOtw/Hp8Ir6r0xOCMl4F0lJuC9DW64bGbWgevEsckQNrTUoMVjx1iAK+AMU23onYAPA8i3oOgEMFrqNkIIP4BHAFxTaCdCiFuFEDuFEDubmorzuWVWJsenQnjsyBTedUEPrObCb3+5WupnJ5SqRgiB4won4Gd3STrw/cN+xR5TbwZnI2iptcNhLc6SUcZsImxq9eBl9gLXhSMTQaxvrkGbz4kxlqAwTNWhdwL+PIANRLSGiGwAbgBw7ynb3AvgvSRxIYCAEGKMiJqIyAcAROQEcBWAQxrGzlQhP316AFazNAFxMViCsjKYmI8jFE8pmoBv7/KCCHhh0K/YY+rNwGykZPmJzNZ2Lw6OziOT4fYbLQlEk5iYj2NjiwdtXgdLUBimCtE1ARdCpAB8FMAfARwEcKcQ4gAR3UxEN2c3uw9AH4BjAL4H4MPZ69sAPExE+yEl8g8IIX6n6T/AVBWheAq/2j2M157ZhiaPfdHtajkBXxHIzYHrm5RLwGsdVqxrqllRjZhDs5GS5ScyW9trEYynMDQXUTgqZink9/aG5hq0+5wYC8RWvNcyw6w0dPcBF0LcBynJzr/uO3l/CwAfKXC//QDOUT1AZsVw955hhOKpRZsvZRxWM+wWEyfgVc6xScmCUMkKOACc0+XDQ4cmIYSANKageokl0xifj5VdAd+Sa8ScR0+DW8nQmCU4OiG9tzc0ezAwE0E8lcFsOIGGmsULCwzDGAu9JSgMowlCCNz29AC2d3pxdpdv2e29TitPw6xyjk2FUOuwLLnaUQ5nd/swG05gaLb6l/1H/FEIUboFoczGFg/MJuKR9BpzdDIEh9WEzjon2n3S9D1uxGSY6oITcGZV8OSxGRybDOHGi3qLqlr6XFaugFc5RyekBkylq9TyCdwLQ9VvRyg7oJRbAXdYzdjQXMNWhBoju/uYTIQ2rxMAMOqv/hNChllNcALOrApue/oEGtw2vHZ7W1Hbe51W+KMJlaNi1OT4lLIOKDJntHjgtJpXRCPm4Gx5FoT5bGmTRtKzBlk7jk4EsaHZAwBoy1bAx+e5As4w1QQn4MyKZ2g2gocOTuCG87uKtlrzOq0IRFMqR8aohT+SwHQooUoCbjGbsK2jdkVUfQdnI3BazWiqQDt8/pp6TAbj+Mj/7eFVIw0IxpIYC8SwoUV6bze67bCaiadhMkyVwQk4s+K5/ZkBEBHefWFP0ffxOm2Y52Siask5oKiQgANAT4N7RWjAB7MWhJXIdN62swufvnYT/nRgAq/9+uMryiHGiCw4oEgVcJOJ0MpWhAxTdXACzqxoook07nh+CK/Z2pLTShaD12mFP8ISlGplwYLQo8rjd/icmAjGkEhlVHl8rRicKd+CUMZkInzwsnW48+aLIARww61PcyVcRY5OLFgQyrR5eRgPw1QbnIAzK5p7940gEE3ivRf1lnQ/r9OKcCKNZLq6E6zVyrHJEOwWEzrqij/pKoXOOieEQFVXHYUQuQq4EuzorsOnrjkDsWQGU0FOBtXi6GQQdovppBOnNq8Do1X8XmSY1Qgn4MyKRQiBHz5xAptaPbhgTX1J9/U6JYt8lqFUJ0cnQ1jbVAOzSR2f7s46KfkZnqvepGc6lEA0mUZ3vXInKTV26bgJxdOKPSZzMn1TYaxpdJ/03m7zOjExH+OJpAxTRXACzqxYHjkyhcMTQfz1JWtL1rj6XDYAgJ8T8Krk2GTopCV6penMVtaHq3gCpOyAouQAHXc2AY/Ejd/APBtOVKV/+Yg/mjsBlGn3OZBMC0yH4zpFxTBMqXACzqxYvvdYH1prHXj9We0l39fL4+irlkgihRF/VLUGTABo9TpgouqugA/OhgFUZkF4KgsVcOMn4F994DDe/O2nEEkYP9Z8RvxRdGStB2Xk/hbWgTNM9cAJOLMieWkkgKeOz+AvX9kLm6X0t3ktJ+BVS9+UlFiqmYBbzSa0eZ0YqeYEfCYKooVqvhLIFfBwFSS1+4YCiCbTeOLotN6hFM18LIlgLHVab0ObV56GWb3vR4ZZbXACzqxIvvtYH2rsFtxwfndZ9/e5pAScNeDVR9+0lICvbVJOWlGIjjpnlVfAI2itdRTtjV8Mbrv0WEbXgCfTGRweDwIAHjw4oXM0xSNPu2z3nZyAy5fZC5xhqgdOwJkVx9BsBPe9OIZ3XtCNWoe1rMeQJSj+CCfg1caJbALeU69uAt5Z56xqDfjQbARddcrJT4AFCUrY4BKUY5MhJNIZeOwW/PnQZNU0Ly6WgNe5rLBbTFwBZ5gqghNwZsXxgyf6QQDef3Fv2Y/BGvDqpX86jHavA06bcpXdQnT6nBifj1WtVeXwXASdCjqgAIDTaoaJjJ+AvzQiTTF9/8W9mA4lsHfYr29ARTKSrXB3nJKAE1HWipAr4AxTLXACzqwopoJx/Py5QfzFOR0lDd45FavZBLfNzAl4FdI3HcYaleUngGRFmBHAeBUmPcl0BuPzsdMSuUohIrhtFsM3YR4YnYfTasb7L14Ds4nw4MvVIUMZmYvCaiY01dhPu00axsMVcIapFjgBZ1YU33+8D8l0Bh951fqKH0uahskJeDUhhED/VAi9ClrrLYbcvDhUhTKUifkYMuL0SqoSuO0Ww1fAXx6dx+Y2D+rcNpzfW4+HDk7qHVJRjPqjaPM6YSrgb9/mc2CsCk8GGWa1wgk4s2KYDSfw02cG8Iaz2rGmsfIErNZp5Qp4lTEXSWI+llLk9V+Oah7GI7u3nKolVgK33YywgZswMxmBl8fmsbXdCwC4cnMzDk8EMThj/BOpUX8U7adYEMp01rkwPh9DPGXc555hmAU4AWdWDD94og/RZBofvaLy6jcgVcBlF5TD40G86VtPYirIgy6MTL9GDiiA5AVOVeoFLo8tP9XOTglq7MaWoAzMRhCKp7C1vRYAcPWWFgDV4YYiJeCFX7O1jW4Igao4kWAYxgAJOBFdQ0SHiegYEd1S4HYioq9nb99PRDuy13cR0cNEdJCIDhDRx7SPnjEK/kgCtz01gOvObMP6Zo8ij+lzWeGPJgAA//b7l7Fn0I99Q35FHptRBzkB10KCYrOY0FrrqEonFLkCroYExWUztgTlwKjUgLmtQ6qA9zS4saG5Bg8dMnYCLuv2Oxd5zeRVH/kYYBjG2OiagBORGcA3AVwLYAuAdxDRllM2uxbAhuzPTQC+nb0+BeDvhRCbAVwI4CMF7susEn705AmE4in8jULVb0CqgAeiSTx+dAqPZ4d1jCpk8xWIJnFgNIAHXp7AscmQIo/JAP3TIZhNpOh0x6XorKvOYTwj/hga3DZFPcBl3AavgB8YnYfFRNjQsjCo6eotLXimb9bQJ1Oybn+xCngvJ+AMU1VYdN7/+QCOCSH6AICI7gBwPYCX87a5HsBPhBACwDNE5COiNiHEGIAxABBCBInoIICOU+7LrAJmQnH84Il+XLO1FZtaaxV7XLkJ88v3H0JnnRMT87GKBl0k0xn8es8wvvnwcQzOnvxF/4p1DXjvRT24anMLLGbdF6aqlhPTEXTVOWHV6Dns8Dnx/Ik5TfalJCP+qCryEwCosZsNPQnzwOg8NrR4YLcsnHy856IefP/xfnz7keP44hvP1DG6xZE/exZLwL1OKxrctqpIwL/6wBGk0hl86ppNeofCMLqh9zd9B4ChvMvD2etK2oaIegGcA+DZQjshopuIaBcR7Zqamqo0ZsZgfPPh44gkUvjEazYq+rg+lw3xVAYHRufxiVefgTavMzcIoxSEEPjN3hFc+V+P4h/uehF1Liv+8bpN+Na7duDXH34FPvmaMzAwE8HNt+/BDbc+g2CMGz/LpW86rEkDpozc+JaqMi/wUX8U7RXYdC6F5IJizEZAIQQOjARy+m+ZNq8TbzuvE3fuGirrGNeCxYbw5LOm0W34BDydEfjJ0yfwf88NVs0AJIZRA70T8NO9lIBTj8gltyGiGgB3Afg7IcR8oZ0IIW4VQuwUQuxsamoqO1jGeAzNRnD7MwN4284uxbTfMrXZYTxb22vxhrPa0e5zlPzlHEumcctdL+Jjd+xFjd2C7793J+75yMW46dJ1uO7MNuzorsNHXrUej37ycvznW7Zj75Af7/7Bc+y+UgZCCJyYDmNNY83yGytEZ50T6YyoKvs3IQRG5tSsgBtXgjIxH8dMOIFt7aevlH3ockm+9t1Hj2sdVlGM+JfX7fdWQQL+8ug8/JEk/JEk+qZZfsesXvROwIcBdOVd7gQwWuw2RGSFlHz/TAjxaxXjZAzKfz9wBETA312lbPUbANq9kt3XLddugslEaC+xAj4eiOHttz6DX+wawkdftR6//ZtX4qotLSA6/ZzSYjbhrTu78K137cDLowG8+/vPwh9JKPa/rAYm5uOIJtNY06iN/htYsCIcMWjVtBBzkSSiybQqFoSAVAFPpDKGnBAqN2BuzTZg5tPhc+It53bi588PYWLeeCdUI/4o6t22JSe8rml0YzIYN3QT7JPHp3N/7x6oPvkWwyiF3gn48wA2ENEaIrIBuAHAvadscy+A92bdUC4EEBBCjJGUxfwAwEEhxFe1DZsxAgfH5nH33hG87+JetHoLe+NWwqvOaMYjn7gcl2yQVk3as6PHi5EbJNMZvPnbT+HoRBDfefcOfOI1Z8BcYHjGqbx6ayu++55zcXg8iJtv3w2p9YEpBrmapmUFXK4iV5MV4WgRldRKcNul1iIjJoEvjcyDCNjcVrhX5MOXr0c6I/DdR/s0jmx5lvIAl1lbBY2YTx6bxobmGvhcVk7AmVWNrgm4ECIF4KMA/gjgIIA7hRAHiOhmIro5u9l9APoAHAPwPQAfzl5/MYD3ALiCiPZmf67T9j9g9EIIgS/ffwgeuwUfvkw555N8TCbKOQsAUgKeEcBkEV7g06E4RvxR3HLtJlyzra2k/V6xqQX/8oateKZvFvfuO3VBiFmME9NSY6sWY+hl5ITIyO4ZpzKsogUhIDVhAjCkDOXoZBBddS7U2Av7D3TVu/DGczrws2cHEDFYI+nI3PK6faM7ocRTaTx/YhYXr2/Eud112MUJOLOK0bsCDiHEfUKIjUKIdUKIL2av+44Q4jvZv4UQ4iPZ288UQuzKXv+EEIKEENuFEGdnf+7T839htOPBg5N49MgU/vbKDfC6rJrsU062ipGhzIQk+UhLbXmV+bef14XtnV78+30HDZnIGJH+6RDsFhPaynzOy8FuMaOl1l6dFXCVNOALFXDjNWIOz0XRvYxF5dVbWhBPZXBkwjj6ZCEERotwrpH9708YNAHfM+BHLJnBK9c34tzeOvRNhTEbZqkdszrRPQFnmFKJJdP4wu8OYENzDW58Ra9m+5UrhsXofadDUpW8scZW1r7MJsLn37AVE/NxfOPPR8t6jNVG/3QYvQ1umIqQ+ihJZ52rqrzAR/xROKwm1Kl04ion4EY8cRyei6JzmST2jBapmfvIeFCLkIpiPppCOJFedtXCaTOj3eswbAX8yWPTMJsIF6ytx7nddQCAPVwFZ1YpnIAzVcd3Hj2OodkoPn/9Vs38ngGgLfvlV4wXuFwBb3Dby97fOd11eOu5nfjhE/04PmWcapxR6Z8Oo1fDBkyZdp9TsQFNWjDqj6LD5yzYDKwENQbVgMeSaUyH4ssm4N31LjitZhwyUAI+UoQFoUxvoxt9Rk3Aj0/jrE4vPA4rtnf6YDERdg9yAs6sTjgBZ6qKodkIvv3Icbxuextesa5R033X2C2odVgwVkSyNROWKuANZVbAZT51zSY4LGb8++8PVvQ4K51UOoPB2YimDZgyrbV2jAdiVdMwO+KPquaAAgBum5SAG01DLcuEZOeaxTCZCBtbanB4oqCrrS6UkoCvaXTjxIzxEvD5WBL7hwO4eL30ue20mbG1w4vdVTjIimGUgBNwpmoQQuDzv30ZZhPhn167WZcY2n3FWRHOhBKwWUyLNnsVS5PHjg9cshYPHZo0rK7TCIz6Y0imRc4FQktaah2IpzJV490+6l9ehlEJNTkJirE04HKjbDHa9zNaPThsoAp4Kc41axrd8EeSmDOYtvrZvlmkM+Kkwsm53XXYN+xHImU8y0qGURtOwJmq4Td7R/HgwQn83VUb0KbSFL/l6PA5MVKEBGU6lECj26bIMv8N53fBbCL833ODFT/WSkW2IOzVKQEHJB9yoyPJMBKqTcEEAHfWBcVoEpSFCvjy//vGFg+mQ4lcL4fejPqjsFlMaHAvv6ImT4LtN1gV/Mlj03BYTdjR48tdt7O3DvFUBi+PGWe1gWG0ghNwpiqYmI/hs795Cef21OGvXrlWtziKrYBPh+Jo9JSv/86npdaBV29pwS93DSGWNFZV0SjITWdajqGXkT3oxw04vOVU1HZAAYzbhDk8F4XVTGj2LO+Ss6lV8gk3SiPmiD+Kdq+jqAbjXAI+ZawEfNfALM7tqYPdsjBI6NweqRFz14lZvcJiGN3gBJwxPEII3HLXfiTSGXzlrWcVNdBGLdp8DgSiyWWrezPheFHVqmJ594U9mIskcd+LY4o95krixHQYNXZL2a4zldAqV8CrYBx9KVricrFbTLCYyIAV8Ajafc6iPj/OaJWcUIzSiDnqjxa96tdV74LZRIZyQhFC4MR0BBuaPSdd31LrQIfPiT3ciMmsQjgBZwzPL3cN4+HDU/iHazbpUuHMR9ZgLteIORNKoKFGmQo4ALxiXQPWNrpx+zMDBW9PpDKYKmJA0EqlbzqMNY1u1Zw9lqIpu9JRVRVwFRNwIoLbbjFgAl689r3JY0eD22YYHfh4IIa2ZaZgyljNJnTVOQ0lQZkNJxCKp9BVwIP9zA6vYU50GEZLOAFnDM2xyRA+/9sDuHBtPW68qFfvcHKVw6V04EKIbAKuXDWWiPDOC7qxZ9CPl0dP10t++5HjuOIrj2A+Vh2NgErTn03A9cBhNaPOZa2KBHxkLgoTLchm1KLGbjFgE2YUnb7ibSo3tnhwaEL/xDCdEZgIxtFWwmu2ptFtKAnKwKzUANtTIAFv9NgM1zDKMFrACThjWELxFG6+fTccVjP+++1naz5gpRDtOS/wxSvgwXgKiXQGjRV4gBfiLed2wm4x4fZnT6+CP3JkEsF4Cn94cVzRfVYD8VQaI/6oLg2YMi21DkxWQwLuj6Gl1qG6f77bbjZUBbxYD/B8zmj14OhEEJmMvvaS06E40hmB1hIaZ3sb3eifDhvGGnNwJpuAN5yegNe7bPBHk0jr/DwzjNZwAs4YEiEE/uFX+9E3FcI33nmObq4np9LiscNESyfguSE8CuuRfS4bXntmG367bxSp9IJtVziewovDAQDAr18YVnSf1cDgTARCQBcLQplWr6M6KuD+iKr6bxm33YKwgXzAZe17Z33x//umVg8iiXTOPUUvxrO9BW21xVfAO3xORJNpzMeM8RoMZivghSQodW4bhEDV2HgyjFJwAs4Ykh880Y/fvziGf7hmk+YDd5bCYjahpdax5Dj6mZA8hEfZCjgAvHprC4KxFHbnjW/ePTCHVEbgvN46PNM3u2RsKxE9HVBkWjwOjAeMr8Ef9cdU1X/LSBIUYyR/QPFDePJZaMTU1yJvLJuAlyIbasx+9hjFRnFgJoLWWgccVvNpt9Vnm9VnWYbCrDI4AWcMxx9eGsO/33cQ125rxU2X6mc5uBjtPifGltCAT+fG0CvvyHHx+kZYzYQ/H57MXfdM3wwsJsK//sU2AMA9L4wovl8jIyfgukpQvA7MhONIpo07UCSTERgLqDsFU8ZlM5YERR7CU4oEZUOLlIDr3Yg5nm34LisBN0hj9uBsGN0Fqt/AQgI+F+EEnFldcALOGIonjk7jb3++F+d01+G/3naWLq4Wy9Huc2J0CRcUeQx9owoVcI/DivN66/HwoZMT8O2dXmxqrcXOnjrc/cKIYbSfWtA/HUaD2wav06pbDK21DggBQzvRTAbjSKaFqh7gMpILinGaMIfnorCYivMAl6mxW9BV79S9EXNsPgab2YR6V/En9LIzz5SBKuDdBfTfAFDn4go4szrhBJwxDHuH/Ljpp7uwtsmNH954Hly2ysa4q0W7z4Exf2zR5ixZA16vQgUcAK7Y1IwjEyEMz0UQSaSwfziAC9c2AADeuKMDxyZDOFDAKWWl0j8d1rX6DQCtXuNbEY7kLAjVdUABjClBKdYDPJ8zWmoNUAGPocVrL6kJXfbDN0IFPJpIYzIYL+iAAuRVwDkBZ1YZnIAzhmD3wBze96Pn0Fhjx0/+8nx4XfpVM5ej3etEIp3BdLjwl9t0KA6v0wqbRZ3D61WbmgEADx+eyum/5QT8tWe2wWY24dd7Vo8MRU8LQhm5smrkYTwLCXjxOuhykX3AjbISMzwXKUl+IrOp1YP+6TDiKf2q+WOBGNpqS4u9zmWD2UQ5OZyeDGXlP8tWwFmCwqwyOAFndOdPB8bxzu89A5/Titv/6gI0l9DtrweyhnYxHbjSHuCnsrbRje56Fx4+NJnTf8sjnX0uG161qQn37htdFbZe4XgKk8G47gl4NYyjH81NwdSmAp7KCMRTxtDElzKEJ5+1TW6kM0JXJ5TxQKxk33aTidDgthlCEjWQsyAsfIw6bWY4rWbMGuBkgWG0hBNwRlduf2YAN9++G5vaanHXh16xaJXESMhetsenQgVvnw7FFfcAz4eIcMWmZjx5bBqPHJ7CmZ1euO0Lcp1Xb2nFdCi+aHwrCSM4oACSl7HVTJiY1z/hWYyRuShqHRZ4HOqvLrltktuFERoxY8k0poLxkhxQZFqyxYBJnV5XIYQ0BbOMwUmNNXZDuKAMZCdyLtaECUgyFK6AM6sN3RNwIrqGiA4T0TEiuqXA7UREX8/evp+IduTd9kMimiSil7SNmqmU+VgSf3fHC/jMPS/h8jOa8fO/vkAV2z41WNvohsNqwosjgYK3z4TVrYADkgwlnsrgwOh8Tn4ic1aXD4CkqV/pGCUBN2Ub/CYMXgHXwgEFQO6E0AiNmDkP8DIq4M3ZZsbJoD6v62w4gUQ6U9bk0iaPMRLwwdkIPHYL6paQFda5rYbSgMeS6VWxgsjoi64JOBGZAXwTwLUAtgB4BxFtOWWzawFsyP7cBODbebf9GMA16kfKKMnzJ2Zx7dcex2/3j+HvrtqAW99zrmEbLgthMZuwtd2LlxZLwENx1RPwC9bUw5n11D01AV/b6IbHbsH+Yb+qMRiBE7IF4SLL21rS6nXkhqYYkRF/eTKMcqjJJuBGaMQsxwNcRtb26yXlkD3Ay62AG0GCMjgrOaAs5WhV57JhNmKMQTypdAaX/efDuOK/HsHPnxvUVf8vE0um8c2Hj+GnT5/Aw4cnc5NFmepG7wr4+QCOCSH6hBAJAHcAuP6Uba4H8BMh8QwAHxG1AYAQ4jEAs5pGzJTN0GwEH//FXrztu0/DbCLc+cGL8HdXbYRF5bHYanBmhxcHRudPq5Kk0hnMRZJoUFGCAgAOqxkXr2+A2UTYmdV/y5hMhG0dXuwfLnyCsJLonw6jzeuA03b6gA+taam1G7oCPqJHBdwA0zBH5sqvgNc6LbBZTJjUKZEdzw3hKT32Ro8N06GE7o2wgzORgiPo86l32wxTAT80HsTEfByxZBqf/vWLuPQ/HsbTx2d0jemPB8bxn388jH/+zQG8/0fP49L/fBhPHZvWNabdA7P4xfODusZQ7SxbdiSijy91uxDiqxXsvwPAUN7lYQAXFLFNB4CxYndCRDdBqp6ju7u7rECZ8jk2GcLtzwzgZ88OwESEmy5di7+5YkOuSlaNbOvw4sdPnUD/dAjrmz2562UdY6PKFXAA+NQ1m/CGsztO0n/LbO/y4odP9COeSsNu0T85VYs+AzigyLTUOvDI4SkIIQznXz8fSyIYS2kyBRNYSMCNUQGPwGKinJ67FIgIzR47JnU6sZKbesupgDfV2JFIZzAfS+nmkZ/OCAzNRfDqra1LbmekBPz5E1JN756PXIzjk2F8/M69+N7jfbhoXcMy91SPPQNzcNnMePDjl2FoNoJ3ff9ZPHFsGq9Yr8+U6KePz+B9P3oO8VQGV2xqyfnOM6VRTAYkZxdnADgPwL3Zy68H8FiF+y/0LXXq6Xox2yyJEOJWALcCwM6dO1nYpQHJdAY/f24Qv9o9jP3DAZhNhLft7MTfXrkBbWVUc4zGmR1eAMCLI4GTEnDZA1wLPfvGFg82tngK3nZ2pw/JtMDBsSDOzmrCVyInZsK47sw2vcMAIA3jiSTSCMVTmjQ6lsKCA4q2EhQjNGEOz0XR5nOU7AEu0+Sx6zbQZjwQg9lEZQ31yg3jCcZ1S8DHAlEk02LJBkxAamIOxlNIpDKq2bcWy64Tc+jwOdHmlX4uP6MJD7w8oeuJ9e7BOZzT7UO7z4l2nxNb22uxa2BOl1h2nZjFX932POpcNozPx/DI4Um8dWeXpjGE4ym4bGbDFTpKZdl3uhDi80KIzwNoBLBDCPH3Qoi/B3AugM4K9z8MIP+V6wQwWsY2jMF4pm8Gn/3NASRSGXzmtZvx9C1X4Etv2r4ikm8AWNeUbcQcPnngjZyAqzEFsxS2Z5PulawDnwsn4I8ksdYgFXC5Uc6IMhRZhqHFFEwAcNulVZeIAZowxwJRtFfwuSNVwPXTgDd77GWdPOTG0evYiDk4K1sQLp2A12WH8fh1dkIRQuD5E7M4r3dB1rejuw5zkWSu4VtrwvEUDo4FcW73Qkzn9tRj35AfCY1tPl8cDuB9P3oerbUO3PvRi9HsseORw1OaxpBIZfCqrzyCG259BoGoMfoGyqWUU81uAPlHRwJAb4X7fx7ABiJaQ0Q2ADdgocIucy+A92bdUC4EEBBCFC0/YfTBn22o+cY7zsEHLllreG/vUrGYTdjSVntaI6b8Zad2E+ZytHsdaKyxYd/QytWB988YwwFFRm7YGw/o3/h2KqO5ITyrrwlzrEwbP5lmj0M/Dfh8tCwHFGChAq5rAp5tFly2Au42xjCeodkoJoNx7Oytz123I9tj88KgX5eY9g35kc4InJsX087euqwLlraf79965BjsFhP+768vRHOtA686oxmPHZlCMq3dicCugVlMBuN4tn8Wb//u04YseBRLKQn4TwE8R0T/QkSfA/AsgJ9UsnMhRArARwH8EcBBAHcKIQ4Q0c1EdHN2s/sA9AE4BuB7AD4s35+Ifg7gaQBnENEwEf1VJfEwyiF/8dY4qlfnvRxSI2bgpJH08pedmj7gxUBE2N7pw74VXAHvm8o6oBgkATfyMJ5hfxRWM6FJo5UZt0EkKJmMwMR8DG0VnHg0e+wIRJOIJbWv5ldy8iBXwPV0QhmYlfT3y0mfctMwdR7GI+u/z8tLdtc31cBjt2DPoD6Sj90DcyDCSVJCufF+t8YylJdGA7hwbUPus+5Vm5oRjKew64R2cTx6ZApWM+G77zkXQ7MRvOlbT+m2OlEpRSfgQogvAng/gDkAfgDvF0L8e6UBCCHuE0JsFEKsy+4DQojvCCG+k/1bCCE+kr39TCHErrz7vkMI0SaEsAohOoUQP6g0HkYZQrFsAl7FjZbLsa3Di3Aijb68g38mnIDFRKh16v9/n9Xpw/GpkCGqkGrQNxWCxUTLVte0orXWuBKUUX8MbV4nTGXqoEvFajbBZjEhpLMLynQ4jmRaVFYBr9UnkZWH8LSWOIZexue0ZsfR61sB76xzLiuhMUoFfNfALGodFmxorsldZzIRzu72YY9OFfBdA3PY2Ow5ScffXOtAd71L08Q3EE1iaDaKLe21ueteuaERVjPhkcOTmsXx6OEp7Oypx2u2tuIXH7wI89Ekvv7QUc32rySldju4AMwLIf4HwDARrVEhJmYFICd97iry9y6VMzulRsx8GYrsAW6E5pDtXV4IIen2ViLHp0LobnDBahAbS6fNjFqHxZAJ+MhcRDP5iUyN3aJ7BTxn41eBBE6WFmktQ5mPpRBJpMs+eTCZCI01NkwH9UtqJQ/w5Veo6txScqm3E8rzJ+aws7f+tBPVc7rrcHh8XvNiRiYjsGdwLieDyWdnTx12DcxqZjP58qjU77Qta0AASMf4BWsa8OdD2iTgE/MxHBoP4rIzmnKxXLiuoWpXeov+5srKTv4BwKezV1kB3K5GUEz1E4qn4LaZNau46cH6pprTJmLOhBKqe4AXy1mdPgArtxGzbyqMdU01y2+oIS21xhzGM+KPataAKeO2m3WfhLkwyKb8/33BTUTb13XBA7z8k4fGGv0cXADJArIY//WcBCWsX1PdbDiBY5MhnFsg2d3R7UNGAPs1ni58bCqEYCx12qwHADi3tw7ToQQGNBrKI+vNt+ZVwAHg8jOacHQyhKFZ9eN49IjU8HnZxqbcdds7vOibCiMYq76GzFJKR28E8AYAYQAQQoxiwaKQYU4iFEutaP03IDVibm6rPSkBn9ZgDH2x1Ltt6KxzVm11YClS6QxOzISxtskY+m+ZVq/xxtEnUhlMBuOaWRDKuG0W3eVPSiSxsgRF6wr4WEBqnK1EPqPnOPpoIo25SBLtRcRvNZvgcVgwp6MERdZT5+u/Zc7pkhJgrXXgssSk0EnBzh4pTq3sCA+MzqOl1n6aw9cVm5oBAA9rIEN59MgUmj12bGpdSD3llegXF5lMbWRKScATQlrrEABARMb65mMMRSieKjggZqVxZocXL4/O5xoxZ0Jx3S0I8zmry7cinVCG5yR/YaNVwFtrHbmqq1EYD8QgBNC5CiUoY4EYrGZCg7v8k+IGtx0mguZWhIpVwHVqwlw4gSjufdfgtmFWRwnKrhOzsJlN2N7pPe02r8uK9c01muvAdw/MocFtK2jjuKG5BrUOC3YPaDMM/MBoAFvbT39u1jbVoLfBhYdVlqGk0hk8cXQal21sOkniuT270luNUstSEvA7iei7kEbB/zWAByG5kjDMaYTiKXhWQQK+rcOLUDyFQ+NBALIExRgVcAA4q9OLEX8UMzouQ6vB8akQABguAe+qd2EyGNfFMWMxRjQewiPjNkACPh6IoqXWUZEUTh6EM6mxBGUsEAPRgga9HBpr7JjRaRx9Tv7jKy7+OrdN1wr4roE5nNnphcNaeHLwjm4fXhic0/S5lPXfhXqKTCbCuT11mjRixpJpHJ8KY9sp8hOZy89oxlPHZ1S1I9w3HEAgmszpv2Xkld79K7UCTtKr/wsAvwJwF6SpmJ8VQnxDxdiYKiYUX/kSFED6UAaA677+OK766qOIJtNoNNBY3u05HXj1fTgthWxBuM5gEhTZkWV4ThtdZjHICbjWGvAau/4SlNFArKIhPDLNtXbNJSgT8zE01tgrmgzZ5MmOo49q/zrICXixz3+9S78KeCKVwYvDgYJaaxmtB/LMhOLonw4vGdPO3nocnQypPsDo0HgQ6YzAlgIVcEDShcdTGVV14I8emYKJgFeubzzttu2d3pVbAc9KT+4RQjwghPikEOITQogHVI6NqWJCsdSKdkCRWd/swV0fugifePVGdNU50eFz4hwDjX7f1uEFEbBX4+YhtTk+FUKD2wafyzirDYBUAQcWJgAaAXkITyVa4nJw2fRvwhwPxCqScMg0exyaS1DGArGK3FsAoDHbjzIV0l4WNZZ93xX7/Ne5bbq5oIz4o0ikM9jQsnhbm+xEopUMRf7MLuSAInNujzbadNnp69QGTJm12ZVINU9OHjsyhbO7fAU/88/s8GFwNqK7i06plHJq/QwRnadaJMyKYrVUwAFpLPBHr9iAH73/fDx5yxW4YG2D3iHlqLFLnrYrzQmlb8p4DZgA0FUvVfuGZqM6R7LAyFwUjTX2RZfW1UJvCYrso63EiUezR/sK+Ki//CmYMk25YTzaJyajgRga3Lai33f1bptuPuDyCfNSMwXWN9XA47DgBY0aMeVkNt+T/FQ2t0oJ8bHJkKqxHBidh9dpXdTRRl6JlFcmlSaSSGHfsB+v3NBU8PazqrQRs5QE/FWQkvDjRLSfiF4kov1qBcZUN6tFA14NbO/0Yf9wQBcdqFocnwoZTv8NSAmPw2oyVgU8EEVHkTpcJamxWxBOpHR7382GE0ikMwpVwO2YCceR0mjkthBCso6sULev5zj6sUC0aP03IFkRxpIZRHQY3jQ4IyWOhZodZUwmwsYWT67/RG0GZiLwOCwnDeA5Fa/LijqXVXUrwpdHA9jaXrvofAufy4Z6tw190+o8N/3TYQiBk9xP8tnasfIT8GsBrAVwBYDXA3hd9jfDnIQQYtW4oFQDZ3V6MRNO5LTA1Y4/ksBMOGHICjiRNJnTSAn4yJz2HuCAVAHPCCCqU0Pqggd45Ql4U60DQkiTbrXAH0kikkgX5aG9FHqOox/LTl8tlvrsMB49dOCDsxHYLabcisFi9DS4NPPdHpyNoKfBtexQt+4Gt6oxJdMZHBwPLio/kVnT6MZxlSrg8mrAmsbCn/lepxVrGt1Vt9Jbyij6AQANAK6H5AfekL2OYU4insognRGrRoJidORGzJViR3g814BpvAo4IC1jazGUohjkSqoSjYilUmOXpAd6NWIu2Pgp0ISZrSRrpQOXT5YrTcC9TissOo2jHw1Ei/IAl5GH8czpMIxncDaCrnrXsm45PfVujAVimrgcDc5GlpTEyPQ2uHBiRj3t9fGpEBKpTEELwnzWNrpV04DL0pbeJaaqntlRfY2YpUzC/CyA2yAl4Y0AfkREn1ErMKZ6CcakL1yWoBiDTW0e2MymqqsOLIa8BLzWoAl4Z52UgBtB8jMVjCOeyuSaQ7VEXgHTqxFzbF65CnguAdfIinB4Lutc46vsdTNlLRS1TsBD8RSCsRTaSpDQ1GftW/XQgQ/ORotLdhulbdQ+wU5nBIbnIuiuX36Vr6fehVF/FImUOvKoAyPSCPrlKuBrm2owFYyrMpGyfzqMdq8DTtvi/QTbO70YDcR0870vh1IkKO8AcJ4Q4nNCiM8BuBDAu9QJi6lm5IoXS1CMgd1ixuY2z4qZiNk3FYbVTOjSQVZRDN31LoQTaV2HisgMZRM5uTlUS+rkhCqs0yAYfxSWbAJaKc1ZNxKtGjGVtI5s9Ng0T0rGynDekRNwrZ0shBAYKrLaLG+jtgxlfD6GZFoUFVNPgxsZoZ716YHReTispmULHmtVbMTsmw5jzTKSw9xAnhG/4vtXi1IS8BMA8o8mO4DjikbDrAhk54MaTsANw/ZOH14amUc6o39VtlKOT4XQ0+CGxVy+P7KayF+acvKrJ/KXcmed9hVwWfYy6tdnMuh4IIaWWgfMFQzhkZG1wZpJUOaicFrNqHMt3oBXLFIFXNukdlT2AC+nAq5xAj4XSSIUTxW1SiRLINSUfADAQBFNobmYslX5AZWq8kcng9jY4ln2OMo5oSjciCmEQP9UCGsblz4B2NpeCxNV18yLUr7B4gAOENGPiehHAF4CECKirxPR19UJj6lGZAkKa8CNw/ZOaWJnn0Yd/GrSNxUy3ACefLobjOMFLksZKtUSl4PsgCGPJNeaMYU8wAHAZjGhzmXVTIIy4o+go865bANeMTTpIEHJeYCX4GNe67DCRNB8GmYxFoQyPpcVHodF9WN7qISYZJnKgEr666GsPn45uupdMJHyFfDZcALzsdSiDZgybrsFaxrdeHl0XtH9q0kpGdLd2R+ZR5QNhVkpyBIUj73y6g2jDGdlhwPtGw4sOWzC6CTTGQzMRPDqra16h7IocrJrhEbModkIGmtscOkwFKvWYUWN3aJfBXw+hi3L6FZLoclj10zKoYQFoUyjR0rAhRCKJPTFMBqIgaj4ITyApFev02EaZikJOBGht8GNEypLUAZmIrCYqCgJT2ONDW6bWZUKeDojNXFfs61t2W3tFjO66l3oU/hEQH685SQoALCmscYQhY9iKfpTWQhx21K3E9FdQog3Vx4SU+2EcxpwbQd/MIuzrqkGbpsZ+4f9eMu5nXqHUzZDsxGkMsKwDigA4LJZ0FhjN0QCPjwXRYcO8hOZNq8jN4lTS4QQGAtEceWmZsUes9nj0E4DPhfNaVorpdljRzItMBNOKKKHL4YxfxRNNXZYS5SJ1bltmlfA5eO02D6J7gYXDqjsNz04K62AFCOzIyLVrAgnStCiA5ITitIV8P7s461dpgIOSI4wTxybQiYjlnW0MQJKiijXlnMnIrqGiA4T0TEiuqXA7ZSVuRzLDgDaUex9GX0IxlmCYjTMJsK2Di/2VZE+rhALFoTGlaAAQHe90xCVmKG5iK7Nqm0+Z86PW0sC0SRiSWWG8Mg0a1QBjyRSmIskFauAy8mTlu/HsUCsJAcUmXqXDTMa69UHZyJo8tiLXiXqbXBheC6q6lCmYptC82NSQ5c+WOLJydqmGvRPh5BRsNeob1pqui/meOhpdCOWzGg+tbZclEzAS37GicgM4JuQhvxsAfAOItpyymbXAtiQ/bkJwLdLuC+jA6EYS1CMyFldPhwcnVfNrkoL+gxuQShjhGE86YzAqD+qSwOmTLvXoYsGfKyMJsDlaKqVEnC17SVHFNbt92QbBwc1GiADSLr/UjzAZercVl004KUkuz31bqQyQlVp1UCpMTW4MTwbVbzJPrc6UORnyJpsAixbgCpB/3QI3fWuolYDerP9N2o3ySqF3jYC5wM4JoToE0IkANwBadBPPtcD+ImQeAaAj4jairyvITg8HsS//u5lQ/gCa0E4noKJAIdV77cXk8/2Ti8S6QwOjwf1DqVsjk+F0FhjX3I8sxHoynrzJjUaXV4IeflYDwtCmXafE9OhBOIpbb3AF4bwKFkBdyCRzsAfUXdQzLBsQajQyUNXvRNE2iUlkvyntCmYMk0ee+6104qSE3CVk7xANAl/JFlyTIl0RvGT3aG5KExU/ImsbEXYr6AMpX86XHTBRXapGViFCXg5gpsOAEN5l4ez1xWzTTH3lQIjuomIdhHRrqmpqTLCrIwnj03jB0/044dPntB833oQiqdQY7do1vDDFMe27CSzA6PVK0M5PhU25Aj6U+mqdyEjpHHceiE7oBRbvVIDuYlM66RqNFC6D/VytNRK+ukJlZ1Q5Aq4Eh7ggNQc11br0KwCPh9NIZJIo91X+nPf2+DGfCwFv0ZV8ERKSlpLGVQlryioZfsnV52LsSBciCkrM1L4NR6ajaDN64TNUlyqKPfmKGVFmM4InJiJFKX/BqTj3Wom1ZtklULJBPwfyrhPoQzt1DLxYtsUc1/pSiFuFULsFELsbGpqKjHEynn/xb24anMLvnz/Qewb8mu+f60JxlLwOIxdoVyNdNe74LFb8FIVJ+CSBaGx5SeAPrrbU5G/yPWwIJSRK2daO6GMB2Iw0YJ/txK05XzN1ZXUjPijsJoJzR7lTh66G1yqJYynsnDyU/r7Tq5gqjXS/FRG/FFkRHEOKDLNHjscVpNqtn8LuuvSTwqUTjyHZiMlfX40e+xw28yKNWLKEz6XsyCUsZhN6Kp3rZwKOBHVEtGXiOinRPTOU277lvy3EOJPZex/GEBX3uVOAKNFblPMfQ0BEeErb92OZo8DH/35HgSi6i5h6k04nmIHFANiMhE2t9fiQBX5pOYzG05gLpI0fAMmYIwEfFjhSmo5yBVorZ1QxgIxNHscig5rkhOREZUHLA3PRdHmdSoyQEimp14dl4xCyDKItnIq4I3aanhLsSCUMZkI3fXqndCUE1NbrQM2iwkDs8o+b0NzpclziAhrm2pwXKF5EzkLwiITcEA6iTsxvXIq4D+CVG2+C8ANRHQXEcllhQsr3P/zADYQ0RoisgG4AcC9p2xzL4D3Zt1QLgQQEEKMFXlfw+Bz2fD1d5yDMX8Mt9y1f0XrwWUJCmM8trV7cXCsOidiyg2Y1VABb6mVlkKHVBoPXQxDcxG01Npht+h3MixXQbVuxBwPxNCioPwEkKrpNrMpp9FWi5G5iGL6b5meRhemQ/HcjAY1kVc72suogHfVuyS9ukYJVDnJrrS9W7Uq68BMBPVuW0mryLmTAgWft1gyjYn5eEmVeEBKlpVawejPfuYX4wEu09MgVcCrIccqJgFfJ4S4RQhxjxDiDQD2APgzETVUunMhRArARwH8EcBBAHcKIQ4Q0c1EdHN2s/sA9AE4BuB7AD681H0rjUlNzu2pwydfcwbuf2kcP3iiX+9wVCMYT6GGJSiGZGt7LWLJTFVOxDyec0AxfgXcbCJ01unrhDI8F9FV/w0ATps0Tn1UYw14uS4cS2EyEdp8DtUr4CP+qOKrFj312jmhjAWisJgITZ7S5T92ixntXqdmFfCh2QhsFhOaS4y1t0E6tpW028uPqdSkFwB66pW1Isz1kJTYxL22yY0RfxSxZOWN1/3TYdTYLSVJyXob3Agn0pjW2M6yHIpJwO1ElNtOCPFFALcCeAyAEkn4fUKIjUKIddnHhhDiO0KI72T/FkKIj2RvP1MIsWup+xqdmy5di2u2tuJL9x/CU8en9Q5HFcLxFGpYgmJItnZIkwGrUQfeNxWGzWzS1VavFLrqXboO4xmajeqq/5Zp8zpzo8m1oBIXjuXo8DkxouL/kkhJHsaKV8DlJj2FJQqFGPPH0FLrKFtCs6ZR/UmTMoMzkk9+qUNbehpcqvlND8yG0VNOAt7gxuBsRLHKr7x6V+rqQG+DG0JIBYBK6ZsOY02juyRDB/m9Xg068GIS8N8CuCL/iuxUzL8HYPxTDINBRPjK287CmkY3Pvp/L6j6Ya4XoRhLUIzK+qYa2C0mHBipPh348akQehtdimpj1aSrTr9hPMl06e4OatGu8TCeYFxy4VDSAUWmw+dUtQI+FohCCOV1+925pET99+NoIFrRc9/T4MIJjZowS7UglOlRye4umc5g1B8rMyYXIok0pkLKnBSU6gEuI1fMh2YrP076ynC96lWpIVUNlk3AhRCfEkI8WOD6PwghNqgT1sqmxm7Bd99zLpKpDG7+6W5FlmqMhKQBZwmKEbGYTdjU6qnKRsy+qXBV6L9luupd8EeSCMa0b7oeD8SQEfpaEMq0+7QdR6+GB7hMR50Tk8G4ar7muSE8ClfAax1W1LmsmiQlY4FYRc/9mkZ31gtb3fqeEAJDs5FcMl0KPSqd0Iz5Y0hnRO6EqZyYlJIZDc1GYLeYSpYSyZ85lfa/xJJpjAaiJTVgAtIxajaRZidxlVB0i3jWBcWbd7mHiB5SJ6yVz7qmGnz17WfjxZEAPvmrldOUmckIhBMsQTEyWzu8ODAaqKr3XCKVwcBspCr03zLyF9GwyprhQhjBglCmzevEfCylSQMgsDAFU60KOKCev3tuCI8Kr5skUVA3KZHlP5VMINXKitAfSSIYT5V1jHT4nLCYSHHXEfnxyqmAK135HZqNZptiS1txbPLYYbeYKpbfDc1GIERpfugAYDWb0FmnXR9BJZTi0fQEgGeJ6Doi+msADwD4mipRrRKu3tKCT11zBn67bxT/++djeoejCJFkGkIANQ6WoBiVre21mI+ldEkMy2VwNox0RlRZBVxeitV+KXShgcoYFXAAmunAx7OOK2pVwAGoJh0cmYuCqDwP7eWQ3CHUfS/OhBNIpDIVnfzIVoRqxyq/huUk4BazCR11TsVXFMrxAJdp90kTT5X6vBmclfTxpUJE6KqvvAF9waGmnBUK7Ww3K6HoBFwI8V0AHwDwGwBfAHCpEOK3agW2WvjQZevwpnM68F8PHMH9L47pHU7FhGJSlYslKMZFnoj50kj1NGIezw52qKoEPLcUq0MFfC4CE6mThJZKboCNRjrwUX8MRFB0kI1Mp096TdXSgY/4o2j22IuePFgKPfWu3GATtRjPrT6UfwIhWxGqXQEfqzDWrjqX4u+DUX8UZhOhtbb0967NYkJrrUOxk8NSPcDz6apzVqwBlxPwUivggORSc6IKrAhLkaC8B8APAbwXwI8B3EdEZ6kU16qBiPDvbzoTO7p9+H937q36SZnyMjMP4jEuZ7R6YDZRVenAq8mCUMbnsqLGbtGtAt7mdcKq4CCacpGrodpVwGNorFEniW31OkAE1bzAR+aiijugyHQ3uJER6lXvgYWBS+WMoZfRyoqwkoFBgDq9DaP+GForcJDp8DkVcR8JRJIIxlJlr6B1Zx2gKkmAB2cjcNnMaHDbSr5vT4MbwVgKcxFjDz0s5RPqzQBeKYT4uRDi0wBuBnCbOmGtLhxWM777np1o8tjxlz9+XrMxvGogJ+AelqAYFofVjA3NNVVlRdg3FUazx17ScAq9ISJ01inzhVgqkpew/vpvYCFp1aoCPjYfU0X/DUhVxhaPel7gI/6oajabciVRzcS20qqyjBZWhKP+GKxmQqO7dL9yAOjwuRRvyB2Zq8wDXvq8qfy9OZjrISmzAl7vQjCeqmjq91DWoaZUDTogVcAB7SaqlkspEpS/EEJM5l1+DsD5qkS1Cmny2PGTv7wAAsB7f/gsplTwF9UClqBUB1uqbCT98alQVVW/ZTrrXLpo7Yfn1EvkSsVqNqGpxq6pBrycJfxi6ahzYsSvfHKYyQiMBZQfwiOjtEtGIUYDUdjMprKqlvn0NqpvRTjqj6LV6yjZA1xGrvKPK3hiOeKvbAWks86F8UAMqXRlMqNyPcDz4wAqsyIcLHMgEaCeTaTSLJuAE9E3iOjrhX4AfEWDGFcNaxrd+OH7zsN0MIH3//g5zVwDlIQlKNXBtnYvpoJxTM5rO6GwHIQQVWdBKNNV76x4KbZUYsk0JoIxQ1gQymjpBS4N4VExAVdpGM9kMI5kWqgmQWmqscNlM6vanDbmj1WU1Mr0NqhvRShNSy3/uZZfJ6XeC6l0BuPzsYrkOx11TqQyAhMVFvByHuBlrqLJiXu5VoRCiLI92gEpbiLgxLSxGzGLqYDvArAbgAPADgBHsz9nA1hZBtYG4OwuH771rh04OBbEB257vuo8wnMSFK6AG5qt7dJEzGqogs+EEwhEk1hbjQl4nQvhRFpTLeLwnGTfJbtJGIF2nwOjAfUr4KF4CsFYCm0qJbGAlOTIfs1KIlfV1aqAExG6612qWhGOVTiER0YLK8JRf2V2ifJ9RxWypJwMxpHOCHT4yj9uZUeXYQUcSHwua9mSPzlxL9cJZSoURyyZKTsB16qPoFKKGcRzW3by5QYArxJCfEMI8Q0AV0JKwhmFedWmZnz1bWfh2f5ZfPhne1TtWleaUHboCNsQGpstuQTc+DrwvpwDSvVJUOQlVC0bMeWqT7lfXmrQ5nVi1B9VfSVgXEUPcJkOn1RlVFomOKzSEJ58uutdqmqrK01qZdS2IkxnBCYq7BWQmzeVasRUooFVln5UWpUfmotW9PnhcVjhc1nL/tyT71fOQCKZNY1uww/jKaUJsx2AJ+9yTfY6RgWuP7sDX/yLM/HnQ5P4f3fuVbzaohbhhFSxZwmKsfE4rOhtcOGlKhhJf2xSckCpRgmKXJGqdCpcKQzk7LuMc8LS5nUglszAr/JKQG4KpsoacACK68BHVBzCI9PTIPkzZ1T4PlEiqZXpqnfBpKIV4XQojlRGVLRSYreY0eSxK9aQW4kvuYz83FfadzI8G6lYwtZd7yrbglU+8arkJGBtkxt908a2IiwlAf8ygBeI6MdE9GMAewD8uypRMQCAd17QjX+6bjN+v38MH79zb8WNFVoQjKVgM5tgt3ACbnS2tntxYMz4FfCnjk+jscaumjZWTRYq4No1Yg7MhOFxWFDnMo4MLLdcr7IMJWctp8IgGxm5Qq10c+3wXBR1LitcNvVWD7sb3EikMpgIKq/HVyKplbFbzGj3OVVrostVmys8WWj3ORV7T8sJeCXvXYfVjGaPvSLnpUxGSE3cFbooddW5yq6AD85GQISKPvPXNEpWhDNh9foIKqUUF5QfAbgAwN3Zn4uy0hRGRf760rX4h2s24Td7R/GxO/YiafAkPBRPsvykStjaUYuh2SgCBvZKTaQyePTwFK7a3FxxY5ce1NilRFhLK8KBmQh6Gsqz71KLBS9wdRsx5Qp4c2151nLFoNY0zEot6IqhM6dbVv5ESKmkVqa3wY1+lSQoStkldviUG3wzMheFz2WF217Z92dnXWVNwhPBGBLp8vXXuTjqnRiZi5a12jI4G0FrrQMOa/mFvDWN0gqgLGE0IqUM4rkUwEYAc9mfjdnrGJX50OXr8JnXbsbvXxzDR362R1HfUaUJx9MsP6kStmYnYhq5Cv5c/yyC8RSu2tyidyhl01XBUmw5DM5GDCU/ARYq4GNqV8DnY2hw2yr64l4Ol006qVLaC7xSC7piULpxMB85qVVq+mpvo0v9CngFemsAaFewt2FUode/o0LrU3m1TgkJSiJd3mrLUAUWhDKyZLF/OlTR46hJKRKUT+b9/DOA3wL4FxViYgrwgUvW4gvXb8WfXp7AB27bhbBBLQqDsRR7gFcJshPKywZ2Qnnw4AQcVhMuXt+odyhl01XnqtiVoFhS6QyGZiPoMVADJiBZ4FnNpPownvFATLEEcCk6KqwynooQIjsFU93XrV3hxsF8FirgypxEdNa54I8kVfmuG/XH4LKZ4XVW9l3VUedELJnBrAIyB6UaWDvrpJOCcnX+gzkLwsrei3ICX47vfCUWhDLtPidsZhP6DNyIWYoE5fV5P1cD2AZgQr3QmFN570W9+M+3bMdTx2fwzu89o8hBrzSheBKeCpfQGG1orLGjtdaBl0aMWQEXQuCBlyfwyvWNcNqqd1Wls16aTqdG49upjAViSGVEzsbNKJhMhJZah+rDeEb9ytjgLUeHz6loBXwukkQ0mVZdguJxWOGxW1TxZB8PxOCwmuBTqPdAzVUT2S6xUpmWUisKQgjFVkA665xIpgUmy3TpGVJAfw3k9b+UeJzEkmlMzMcrLiKYTYSeBtfKkKAUYBhSEs5oyFt3duG77z4Xh8aDeMt3ntLU3qwYwvE0a8CriG0dxp2IeXgiiBF/tKrlJ4BUyUukM2V/IZaC7HtbiX2XWkjL9SpXwOc1qoD7XBhR0FZRTuYrccAolnaVBgmNBWJo9zoV6z3oyFbrR1R4z4wGlKk2KzWMZz6WQiieUkaCkmsSLtMCcC6CtloHbJZK0kNptYWodAtWOW4lPsPWNrlV9ZKvlFI04PkTMf8XwOMA9pW7YyKqJ6IHiOho9nfdIttdQ0SHiegYEd2Sd/1biegAEWWIaGe5cVQjV21pwe0fuADTwTj+4ptP4vkTs3qHlCMUT1XcRMJox5Z2L45PhRBNGK+v4MGXpQW2KzY36xxJZXRpaEUo23f1GDABb1N5GE80kYY/klTVAUWmo86JSHZ/SpAbwqOB00+bz6GOBCUQzXljK0G7ig2jYwqtlCgVo3wCpowERTr2y9WBD81G0KmAhM1uMaOt1lFyAq6UBAYA1jTWYGAmbFgb51JOceSJmLsBPA3gH4QQ765g37cAeEgIsQHAQ9nLJ0FEZgDfBHAtgC0A3kFEW7I3vwTgTQAeqyCGquW83nrc/ZGL4XVa8c7vPYM7nhvUOyQAsgacE/BqYVt7LTICODhuvCr4AwcncXaXD80e9SuaaiJ/kWjhhDI4G4HdYkKLAZ+zNq8TE/Mx1aQ44/Pqe4DLKD2GfFjjCrgaEpQxf0zRk59mjwNmEymegCdSGUyF4orEWueywmE1VRzjqIIe8LlpmOVWwGcrG8JzUiz1rpILD4MKeIDLrG10I5kWmrpQlUIpGvDb8n5+JoR4ssJ9Xw9AtjG8DcBfFNjmfADHhBB9QogEgDuy94MQ4qAQ4nCFMVQ165pqcPeHL8aFaxtwy69fxGfueVH30fXheAoelqBUDVs7sk4oBtOBT87HsG/Ij6u3VLf8BFhI1rTwAj8xHUZ3vcuQlo3tPgeSaYHpkDpSnJwHuIJV2MWQmxmVSmRH/FG4FWgKLIYOnxOz4YSiq16pdAaTwZhiFoSApOFtrVXO5k9mYj4GISp3QAEAIpL6ASpNwAPKuLIAkhd4Y42trJhiyTQmgrGKHVBkJC/w0uIYnI3CZTOjwW2reP9rs9OTjdqIuWwCTkS1RPQlIvopEb3zlNu+VcG+W4QQYwCQ/V1onbkDwFDe5eHsdSVBRDcR0S4i2jU1NVVWsEbF67LiR+87Dx+8dC1uf2YQb/72U6pZNy1HKp1BNJmGW8VBEoyytHsdqHNZDacDf+jQJABUvf4bkL4QW2rtmvRrGNGCUEauOKrlhDKukLdzMcg683GlhrBkPcC18G7PebIrKAeaCMaREVBkCE8+HT6n4hXwBQtCZWJtVyDGkbkobBYTGt3K+NeXa0Uo9TUAXRUO4ZHprndhfD5WUmFwcFYqIihxLMhe4P0GbcQspgL+IwAE4C4ANxDRXUQkv0suXOqORPQgEb1U4Of6IuMr9AqUvH4phLhVCLFTCLGzqamp1LsbHovZhE9ftxnff+9ODM9F8bqvP4F7XhjRfARrOC4dZNyEWT0QkTQR02AJ+K4Tc2issWFjS/WNny9EV13pS7GlIoTIDeExIgvDeNRZCRjTYAy9TKNbslVUsgKu1aRXNbzA5ddUaQeadp9D8cZdpYbwyEgV8MpiHPFH0e51KLZy1VnnLCsBl4sESklQ5M+iUooPSlgQytS7bah1WAzbiFlMAr5OCHGLEOIeIcQbII2g/zMRNSx3RyHEVUKIbQV+fgNggojaACD7e7LAQwwD6Mq73AlgtIiYVyVXbWnB7//2ldjQUoO/+8VefOC2XbmqkBaEEpJfK9sQVhdb22txeDxoqCmrB0YD2NruNdQ0x0rorHOqLkGZCsYRTaYNm4ArrZs+lfFADD6XVRPLStlWUanP1xG/+lMwZWSfbiUry/KqhlJVZRlJr66shaeScg/pcZyYDsUrkn+O+KOKPnedWVlMqc/bkIINkMDCMJxjk8UNwxFCKJqAExHWNtWgz6DDeIpJwO1ElNtOCPFFALdCan5cNglfgnsB3Jj9+0YAvymwzfMANhDRGiKyAbghez9mETrrXPjlza/AZ167GU8en8bVX30UP31mACkNkqtQTErA2QWlutja4UUincGRiaDeoQAA4qk0jk2GcoOCVgJd9S6MBaKqnuScyDmgGFOC4ss2rKnRAAhIlU0tqt8ybV5lXF3C8RT8kaTqQ3hkWrx2EEFRRxr1KuBOxfsGxvwxeJ1WuBSSSsqJcyUnY0pNwZTprHMikcqU/LwNZaUwTTXKSGHWNUufRcUm4FOhOGLJjKI2qmsb3VUtQfktgCvyrxBC3Abg7wFUMgnmywCuJqKjAK7OXgYRtRPRfdn9pAB8FMAfARwEcKcQ4kB2uzcS0TCAiwD8noj+WEEsKwqzifCBS9biDx+7FGd2evHP97yE1379CTx5bFrV/YbikiUXS1CqC6NNxDwyHkIqI7C13at3KIrRVedCRlT2Jb0ccu+H0aZgyhAR2r1O1cbRj89rM4RHptXrVOT1HFHQAaMY7BYzmmrsilbAxwIxeOwWeBzKNpGqsWoiD+FRikpjTKSkGQGKVsDryhuCMzQbQVedUzEpjMtmQYfPiWNTxSXgcqIua7eVYE2jG6OBmCGtdovJlCYAbCei7QVu+3a5OxZCzAC4ssD1owCuy7t8H4D7Cmx3N4C7y93/aqC30Y2ffeAC/PHAOL5430G86/vP4pINjfjQZetw0boGxZf3Q7IGnCvgVUVvgxsumxkHRufxVr2DgSQ/AbCiKuCd9bITSkSx5d1TGZyNwGwizRK5cmhTQdMrMx6I4cwOnyqPXYg2rwN/OhCDEKKiz1LZA1orDTggNUsquRIx6lfWA1wmX69+Trcyj6nUyHeZShPw8YDkyqLkcSs/1og/inN7Co5YKcigCp9P65trcHSiuAT88Li0CntGq0ex/a/NymD6p8PYYrDvlGIq4DUAPAB2AvgQJBeSDgA3Q/LmZgwMEeGabW144P9dhk9fuwkHx4J45/efxev/9wn8avcwQvFUxftIpDL43f5RfPWBIwAAr5MT8GrCbCJsavXg5TFjVMAPjM6jxm5RTAdoBLpyFSn1GjFPzETQ4XPCaq5sgp2atKlUAY+n0pgOJbStgNc6EE9lKh7GM+zXzgNcpsOnrL3fWCCGVhXcZ2SdtrJ69ahi+m8gT9JTZoy5FRCFJShA6VMopQq4sp+7G5olDXYxevQjE0HUu22KSWCAPCcUAzZiLpspCSE+DwBE9CcAO4QQwezlfwHwS1WjYxTDYTXjg5etw42v6MU9L4zg1sf78Ilf7sNn7nkRV29pxTVbW7Gztw4tRWooY8k0numbwZ8PTeL+l8YxFYyjq96Jz7x2M9Y2rgznitXE1nYv7nlhBJmM0N1D+sBoAFvaanWPQ0navNJQETUbMQdnwoZtwJRp9zowGYwjmc4oeqIwEZC0rlqMoZdZsPOLoa4Cz+LhuQhsZuV0t8XQ5nXiz4cmK67ey4wFYqqsWHkcVnjsFsVOFtSYllqppGdEYVtEQJJ+tNTaS0o6A5Ek5mMpxQsf65trEEtmMOKPLltdPzQexMaWGkVX53sbpX32G7ARs5RSZTdO1nwnAPQqGg2jOg6rGTec3423n9eFPYNzuPuFEfx+/xh+u08yl+nwObG1vRYddU60e53wuaxIZQSS6QwCkST6p8Pomw7j8HgQ0WQaTqsZl2xoxDsu6MZlG5pWVNK0mtjSXoufPjOA4bmoog0wpZLOCBwcC+Lt53Utv3EVYTGb0OZ1qF4Bf/1Zbao9vhK0+ZwQQhqG0qlgpS03hEdTDXjWC3w+WtHS9sicVJHV8rOz3edELClV7ys5eQCkYsx0SFkNcz5K+GzLKO2AItNRV/4wnuG5CIiUf++uaXSjr0jtNbCwOqeUB7jM+mapIHd0MrhkAi6EwJHxIN66U9nPfpfNgnavA30GbMQsJQH/KYDniOhuSF7cb8TCJEumyiAinNtTj3N76vG512/FSyMB7Bn0Y8/AHI5MBPHksWmECzQttHsdWNPkxg3nd+GyjU24cG0DHFb1bb8YdZGrVwdGA7om4P3TYUST6RWl/5aRpsKpk4DPhhMIRJPoNagDioycZIz6lU3A5TH0WgzhkZH3VamWWksLQhl5YuWIP1pxAi77TaslGWv3KeM2A+Tr7ZWNtbPOhf3D/rLuOzATQbvXqfj36NqmGtz34ljR28ufTUoel8BCAn5sMoQrNi0+WG14LopwIo2NLcrpv2XWNLkNOQ2z6ARcCPFFIrofwCXZq94vhHhBnbAYLbGaTTinuw7ndNfhr165BoB0NjofS2E+moTVbILVTHDbLZxsr1A2tnhgNhFeHpvHtWfqV0VdaMBcOQ4oMl31TjxyWJ1JvAez+v1NrcY+cZF1rkrrwHNDeDSsgDd57DCbqGInlJG5KC4/Q9sBce2+hZOHbR2VHWsL3tHqVcD3DQcUeSz5ZEHpE56uOifuf3EMqXQGlhKlVQMzYVVOXtY2uuGPJDEbTqC+iJMsuQKudAHG57Khsca+rBXhQgOm8hLWf37dFjgsxstdSuqWE0LsgTSIh1nhEBG8Tiu8TmVtpRhj4rCasa7JrftEzJdH52Ezm7BhhUzAzKerzoXJoDSwQ+kT2VwC3qZ89UhJ2lSYwghIThIeh0VTByazidDssVdUAY8l05gMxjVvOF5wF6n8RCgnXVC4cirT7nNiNpxANJGueMjSiD8Ci4nQ4lFWb99d70IqIzAWiJXsIjIwE8Grty5eGS4XeQhO31QI9e76ZbcfnI3A67SiVmErSUBqxDy6XAKenUOhRgXcqIUJ47bLMwyjKVvbvbp7gR8YncfG1hpDO3mUi/zFXM6I6OU4OBZEk8eORg0b+cqhxm6Bx2FRoQKurQe4TKu3smmYSk8eLJYGtw02s0mZBHw2ArvFhCaFk1oZedVECRnKyFwUrV5HyVXq5ZBPoErt8QjGkpgJJ9Bdr7x0bG2T9JjFap+HZqOqnQiub67BsckQhFjcCeXweBAdPqfiXvJGZuV9yzEMUxZb2moxPh/DjIJT50pBCCGNoG9befITIM8aTIVGzEPj89ikoHeumrR7napUwNWwwVuONq+jopOJAZ2ml5pMJHmyK+AFPjQbRWedU/G5EjJKVuuH55SdOCkjn0CV2uMhv/69KvTdSJakhONFun/0TYdUc1Fa31yDYCyFqeDi3y1HJoKK+n9XA5yAMwwDIG8ipk5+4GOBGOYiSWztMOZyYaXkKuAKN2Im0xkcnQhhS1t1PG9tvsqS1kKMBWJo03AMvUxrrTTQZqnK3lIMZN8Lenjet3kdiklQ1KzgK+kFPuKPKt5kCCzYjA6WmYCr0fhuMZvQ01DcGPZgLImh2Sg2q/QZsiHnhFL4ZCCZzuD4VEgV+YmR4QScYRgAyFmp6aUDl/e7EhswAaCpxg6bxVTyeOjl6J8OI5HOGF7/LSMN41GuAp5MZzAVimvagCnT5nUgkkgjWOZAs6HZCDx2C+pc2i+7t/ucGFNIgqKW/hsAWmodMBEwUuGqSSKVwcR8TBXHGYvZhA6fE4Ml+vwPzErJsVorIGsbi3P/OJLVX6u1ipbvhFKI/ukwkmlRNat4SsEJOMMwAKRu9Q6fUzcd+EsjARABm6skkSwVk4nQWefEsMISFLkBU63qldK0ex25pjolmAzGIYS2HuAy8vj1cnXgAzNhdDe4VJNvLEWHz4nx+RhS6UzZjxGISsNb1HJAASSXrpbayqv144EYMgLoVMmvvLu+dJvRgekIGmtsqjUPr22qwcBMeNnX+OBYNgFX6TOkyWOHx2FZNAE/NK5eA6aR4QScYZgcm9tqc1aAWvP08RlsaauFy6adk4XWSF7gylbAD44FYTVT1UygbVfYinA8+zh6VcCB8r3AB2YjushPAGklIiOAiSV0ucuRayJVsQIOKDOMZ9gvxaqW53pXOQn4bFhV/f/aJjeSabFs4/eh8Xl4HJacP7zSEFHWCSVY8PYj40GYTYR1zcaeY6A0nIAzDJNja3st+qbDiCTKW1Ivl0A0id2Dc5r7IWtNV71T8SbMg2PzWN/sgc1SHR/nctVYKRmK/DhaDuGRkRs/y5FyZDICw7P6TZ7NvQ4VJLbDc9q4uCiRgMtDeDpVS8CdmAknEC5BjjQwE0GPis/d2sasE8oyjZiHxoLY3Fqr6kqM5IRSWA5zaDyINY1u2A3o1a0m1fGJzTCMJmxpr4UQC0uCWvHksWmkMwKXbWzWdL9a01nngj+SRDCWVOwxD43PY3MVaSflaqlSUhxZ/tGm8HjxYmj22EFU3snE+HwMiXQGPSpY0BVDuwKTPAc1slFszzq2ZDLlNbsCkgOKNPJdPQkKULzLUSyZxlggpnIFXPYCX1wHLoTAofGg6j0kG5o9mA7F4Y8kTrttNTqgAJyAMwyTR24k/Yi2MpRHDk/C47BgR7dP0/1qjZx8KiVDmQ0nMDEfrxr9N1C+Y8RijPpjcNvM8Gg4hEfGajahqcZelgY854ChkwRFluxU5mMeRa3DovrAtg6fE4lUBjPh05O3YhnxR9Hssau2UiS/joMzxb2vZbmKWtZ/AFDvtsHnsuL4Egn48FwUoXhK9WE18sTVx49On3R9OJ7C4GwEZ6wy/TfACTjDMHl0+Jyoc1nxooYJuBACjx6ZwiUbGhUfkGE05GY1pWQoh6pkAmY+FrMJ7T6HYich4/PScBU9GhmBrBf4fOlJrBYJ2FLUOixw28wVDbhR24JQRq7WVyJDGZlTx4JQRj65LvbEcsEDXt3nb22jG31Ti0tQ5NVOtT9DLlhTj846J+54fvCk65/rnwVQPU3kSrKyv+0YhikJIsKZnT68OKKdE8rBsSAm5uO4fIXLT4B8+YUyyafs2W7UUcuL0VXnUqwCPhaI6aL/lpGmYZb+eg7MhmExkS7uLYB0rCsxyVPtBkxAmWE8w/6IKkN4ZHwuKzx2S9HH9okZqSrdq/IQprVNNehfwopQPolXuwJtMhHecX43njw2gxN58Xz70eNo8zpw2caV3f9TCN0ScCKqJ6IHiOho9nfdIttdQ0SHiegYEd2Sd/1/EtEhItpPRHcTkU+z4BlmBXNmRy2OTgQRSypjE7ccjx6ZAgBctsIbMAHpS7rGbinZLWExDo0H0VhjV20MuFp017sU1YDr4YAiU66v+cBMBB11Tl1Xfdp9zrKnYQohuWuoaUEoIyfOI2Um4OmMwJhfHQ9wGSJCV33xJ5YDMxF4HBb4VPaAX9PoxmQwvmjfyaHxIHoaXHBrIOF667mdMJsIdzw/BAB4/sQsnuufxV9fsrZqmsiVRM//+BYADwkhNgB4KHv5JIjIDOCbAK4FsAXAO4hoS/bmBwBsE0JsB3AEwKc1iZphVjhndniRyoicv3QhfvhEP/7tdy/jW48cw53PD2G+gqbCRw5PYnNbLVp0mGSoNUTKeoEfHJuvSt/0rnoXpkOlOUYUIpXOYDIY162KDEgV8GAshVCJ/8uQjhaEMq215VXvAWAqGEc8ldFEglLrzMplyhzGMxmMIZURqjmgyHSXkoDPRtDb4FZdOrWuSaqwL1YFPzg+r9kAnOZaB67c1Ixf7R5CIpXBNx8+hnq3De84v1uT/RsNPRPw6wHclv37NgB/UWCb8wEcE0L0CSESAO7I3g9CiD8JIeRPvGcAdKobLsOsDuRmmZcW0YFHE2l84Xcv44dP9uM//nAYn7prPz76fy+Uta9gLIndAyvffjCfToW8wFPZEfTVqJ2Uk7b85fpMRuDrDx0tSWYwHUognRE6V8DLa2bU0wNcps3rwGQwjmQZw3jkPgYtJChEVJEVofw+U1OCAmRtRmcjEGJ5txZ5CJPaLOWEEk2kcWI6rKmE7R0XdGM6lMD/PHQEjxyewl+9cg2cttVlPyijZwLeIoQYA4Ds70IC0A4AQ3mXh7PXncpfArh/sR0R0U1EtIuIdk1NTVUQMsOsfDp8TtS7bYs2YsrV2/9++9k4+IVr8PGrN+KxI1PYPTBX8r6ePDaNVEbg8lWk/5O9wIv5kl6KXQNzSKQzOKvTp0xgGtKVrUTmVwsPjQfx1QeO4J69I0U/jjzMR9cKeK3sa158chiIJuGPJHVrwJRp8zkhhDRNtFTkk0gtJCiALJcpLwFX2wNcprvehXgqg6llns9kOoORuSh6NXj9expcMBFweOJ0a9mjk0FkhLbThy/d0IQOnxPffPg4PHYL3n1hj2b7NhqqJuBE9CARvVTg5/piH6LAdSd9axHRPwFIAfjZYg8ihLhVCLFTCLGzqWn1fNEzTDkQEbZ1eBdtxMz3/nXazPjAJWtQ77bhaw8eKXlffzwwAY/dgh09BVtAViTd9S5EEumKLNUA4Hf7R+Gwmqpy9SDnmZyXgMsNpQPTxctz5Kpza61+TZi5yZ4lyCPk/1vvCviCFWHpia38P6jpLJJPJRVwWTvernoFvDgnlFF/FKmM0MQD3m4x47zeevzpwPhpJ/2H5BH0GlbAzSbC28/rAgC856Ie1S0sjYyqCbgQ4iohxLYCP78BMEFEbQCQ/T1Z4CGGAXTlXe4EMCpfIKIbAbwOwLtEpeUkhmFynNlRiyOLNGKeOn7aZbPgg5euxeNHp7F7YLbofYz4o/jtvlG8aUcHrCvcfjCf3ux0uhOnaDJfGgngiq88sqj0J59UOoP7XxzHlZtaNGmeUpp6tw0um/kkO8YDo9L/3T+zuGPDqSxMwdSvAt5S6wBRaQ2CCx7g+o7eXrD3K11bPTgbQZPHDodVG/lAh8+B6VCirObw4bkIGtw2uGzqHivdRSbgWlkQyrz+rHYcnwqfNmDt4Pg8nFaz5ieC77mwBzde1IObLl2r6X6Nhp7fevcCuDH7940AflNgm+cBbCCiNURkA3BD9n4gomsA/AOANwghlJ3tzDCrnDM7fEgv0og5OBuF02pGY40td917LupBg9uGrz14tOh9fO+xPgDATZetqzzgKmJhPPTJieZTx6fRNx3GX/9kFyaX8ZV+pm8WM+EEXre9TbU41YSI0F3vOqkCfmA0WwEvIQEfn4/BYTWp7iSxFDaLCc0ee0kSFDlB02sMvUwlw3iG5rTVsOdWGsqIdXguqqoDikxHnRNEyw/akt/jak7BzOfaba0wmwi/2z960vWHxqQJlCaTth76dW4bPn/9NvhctuU3XsHomYB/GcDVRHQUwNXZyyCidiK6DwCyTZYfBfBHAAcB3CmEOJC9//8C8AB4gIj2EtF3tP4HGGalcmbn4o2Y0vAN50nd+y6bBR+8rPgq+HQojp8/N4g3ntOhemOU0ejwOWEx0WkV8OOTYdTYLfBHkvjrn+5estL3u/2jcNvMeNWm6vVO76pfaEbNZAQOjs7DbCJMzMcRSRTnKCJ7gOs1hEdGkkcUnxgOzobR4LahRufVi0qG8QzNRnNafi2oxAt8xB/V5HPGbjGjtdaxbAX88EQQNXYLmjWyD22oseMV6xrwu/1jORnKsckQnjsxi/PX1GsSA3M6uiXgQogZIcSVQogN2d+z2etHhRDX5W13nxBioxBinRDii3nXrxdCdAkhzs7+3KzH/8EwK5F2r2PRRszF7NPefWEPGmts+Ny9BxBPLb1M/MMn+pFIZ3Dz5aur+g1IkyC7G1yn2YIdmwpha3stvnbD2dg35Mcnf7W/YKNmMp3BHw6M46otLZot/6uBPIxHCIGhuQiC8RQuWtsAYGGJvhD37hvFfz9wBD94oh8HRgO5Jkg9KVWfPDAT0b36DZQ/jCeWTGMsENXEglCmXC9wIUR2CqY2JwuSF/jSqzh7h/zY3unVtPL8+u3tGJiJ4KVsb8+X7z8Ep9W86mUgerJ6hJcMwxSN3Ii5f/jkBFwIgaHZSMHGK5fNgn9/45l4aWQeX7rv0KKPHYgm8dOnB3Ddtjasy1pkrTbWNLhPSsCFEDg2GcK65hq8ZmsrPnXNGfjtvlHc9tSJ0+775LFp+CNJvG57u4YRK093vRPRpNSMKstPrjtTktQsJkOJJtL4+zv34n8eOop//d3L6JsKY0OL/u+hDp8TI/5o0c42g7MR9OjcgClTzjCe50/MIiOAc7p96gRVAFlrX2oFfDqUQDyV0WylbX1zDQ6NB5HJFH4vxJJpHBoL4uwunybxyLxmayusZsJv94/i6eMzePDgBD50+To01lTXEK+VBCfgDMMUZHuHF0cnQydJIWbDCYQT6UW1n6/e2or3X9yLHz91An94abzgNrc+dhzBeAofWoXVb5k1jW6cmAnnvqRnwgkEokmsz56QfOiydXjVGU349/sP4cgp9mG/2z8Gj8OCSzc2ah63kuQ7RryclZ+8emsLAKB/ESeUPYNzSKYFfnDjTuz77Kvx+Kdehc++bkvBbbWkzetAPJXBbBHONqF4CqP+aK4ZV2/KGcbz8KEp2CwmXLRWu/egrLUvNQGXbVM7NHJr2dFdh2AshWNToYK3HxgNIJUROEvjBNzrsuKSDU34/f4x/Pt9B9HmdeCvXrlG0xiYk+EEnGGYgmzr8J7WiDk0J3v/Lv5l9ulrN2N7pxef+tW+k5rsMhmB/+8Ph/DNh4/j+rPbcwN/ViO9jW7EkhlMBKXK4/FJ6ct6XbOUgBMR/uMtZ8Fjt+Bjd+zNSXrGAlH88cA4Xr2lFXZL9cpPgJOtCA+MBrC+qQaNNXY01tgWrYA/0zcDs4lw/pp6eF1WdNW7dB3lLrOgT16+krxvyJ+tHhvDerOcYTyPHJnEhWsbNB+gUqrWHgBOZN9LazQ64dmRXRXYs8hchL1D0qriORon4ADw+rPaMOKP4sWRAD75mjOqWsK2EtD/k4thGEOyPduImS9DGSzCv9hmMeEb7zgHQgDX/s/juOWu/XiufxYf+8VefPuR43jnBd34r7eepW7wBkd2QunPTqeTq2Xy2GgAaPLY8R9v2Y6DY/P419+9jC/89mVc9p+PIJ7M4J0XVP/oZlnGJCXg89jaLnkR954iz8nn2b5ZbGuvhcdhLO9gWd5QTDOjnJhpLUFYjFKH8QzORNA3FcardPCfL8cLvH8qDBNp57m+ptGNOpcVewYXS8D9aPc60KxD78JVm1tgt5iwraMWf3F2oZmGjJZUn4EswzCa0OZ1oM3rwK6BOdz4il4A+cM3ltZT9jS48YsPXoQfPtmPe/eN4o7npYG2t1y7CR+8dK3urhV605tnRfiK9Y04PhmG02rO+TLLXLm5Be+6oBu3PzMIEwFvObcTf3vlBs2Gn6iJ02ZGk8eOFwb9mAzGsSWbgPc0uPHksenTto8l09g75Mf7L+7VONLlKcWhY8/gHDY01xhmAEn+MJ5idNKPHJFGdlx+hvYOPB0+Jx58eQJCiKI/Q/qmw+isc8Fm0abeSEQ4p7sOewb9BW/fOzSnufxExuOw4sfvPx8dPqfm1oPM6XACzjBMQYgIO3vr8Xz/bO4Lb3gugsYaW1HDX7a01+Irbz0L//KGrfjTgXG01jrwivXVrVtWitZaBxxWU86K8PhUCGub3AW/FD/z2i3oaXDhys0tK65ptavOicezybacgK9pdOGuPTFEE+mTJA57BueQSGdwwVrj2abVuaxwWE3LJuBCCLww5MdrtrRqFNny5A/jObeIqeAPH5pEb4NLM0lHPu15WvuGIpsH+6fDmse6o9uHPx+aRCCShDfPo34mFMfQbBTvvkC/8esXrWvQbd/MybAEhWGYRTmvtw7j87Gc9dfgIg4oS1Fjt+BNOzo5+c7DZKKTpBbHJkNY31w4uXbazLjp0nUrLvkGJFlAIiVpj7e2SZIneTjJwClWbs/0zcJEwM5e4yXgRIR27/L65L7pMPyRJHb0+LQJrAhKGcYTS6bxdN+MLtVvoDStPSCd8OiRgMv6/heGTpah7Bv2AzCO/IjRF07AGYZZlJ09UrKz64T0RTI0G9V8bPFKZU2jG/0zYUQTaYz4oysywV4OuZm3s86ZqxTKydKpg4qe7ZvB1nYvag2m/5Zpz1oRLsXurP57h0EaMIGFYTzFTJh8pm8GsWQGl+mg/wYWEvBivcAng3FEEmmsbdI2AT+rywcT4TQZyt5BP0y0MOiMWd1wAs4wzKKc0eqBx27B8ydmkUpnMOKPoqt+dU2uVIveRjcGZyI4OinZDK7mBFxuwAQWxrOfyBvGE0um8cKQHxcaUH4i0+5zLCtBeWFwDrUOi6Fea3kYz1gRDaSPHJ6C3WLKDUzSmo4Sp2H2TWnrgCJTY7dgY4sHL5zSiLl3OICNLR64bKz+ZTgBZxhmCcwmwo6eOuw6MYexQAzpjEDXCmgANAJrGt1IZQQePTwFAItKUFYy8ntpS9tCRbDWYUWD23ZSBXzvkB+JVAYXrDGufrXd58RUKJ6T1BRiz4Af53TXGa4Brt3nLKoC/uiRKVy0rkE3+zqfywqn1Vx0Ai5LvPTQq+/oqcPeQX/O618IgX1Dfk2HFzHGhhNwhmGW5LzeOhyeCOKl7Fh6lqAog5wUPHhwAiYCehtX3/O6pb0W2zpqceXmkzXFvdlBRTLP9M2ACDhvjZEr4JKd38R84UR2PpbEkcmgoeQnMq21y1fAJ4Mx9E+H8UodezmISFppKHJw0ImZMGwW02nuQlqwo7sOwXgKR7Me//3TYQSiSdZ/Mzk4AWcYZknkprd79o4AWHoID1M8cgK+bziA7npX1Q/WKQev04rf/c0lpw1l6mlw4UTeNMxn+mawpa3WMNZ9hZCTvMX0yfuG/BAChmrAlClmGM/kvOQTrvfxL2nti2vC7JsKY01DYXchtckN5MnKUPYO+QFANwtCxnhwAs4wzJKc1emDxUT486FJmE2ENq/2AyRWIg1uGzxZO0cjaYKNwJoGN8bnJSvChw9PYteJOVxscBeddp90XCwmj9g9MAciYzpgFDOMZyp7W5OnOPs/tegoYRhP/3RIF/kJsDCQ5w8vjePTv96Pf7r7JdS7bdjQ7NElHsZ4cALOMMySOG1mbOvwIpkWaPc5DDH6eyVARFiTdWdYtwr130vRk02abnv6BD74k93Y1ObBhy9fp3NUS7PcMJ49g36c0eIx3BRPALmT6qUS21wCXqT/tlq0+5yYCsYRS6aX3C6VzmBwNpI7xrSGiLCjuw6PHpnC3S+M4A1nteMXN10Is8H0/4x+cCsuwzDLcl5vHfYO+Vn/rTC9DW7sHw5gPVfAT2JN1gv8y/cfwraOWtz+VxfA57LpHNXSOKxmNLhtBeURmYzA3sE5vHZ7uw6RLY98XA/MRHDeIj7rUyFjVMDlKbInZsLY1Fq76HYj/iiSaaFbBRwAPvGaM3D5pma8fnub4d+/jPZwKYthmGWRdeDsgKIscnKwrlm/JMGI9DS6YDZR1STfMu2LyCOmQ3HMx1LY0mZM+UFnnfR8D8yEF91mKhiHx2HRzQFFZkN2tejoRGjJ7fp0dECR2dxWi/dc2FM1719GW7gCzjDMsuzsqYPFRKvSKk9NLjujCY8emVqykrcaqXVY8aubL8L65hpDSjYWo93nyHlP5zOcTco76ozpoW+zmNDhc57kvX4qU8G47tVvQEqoTSRNj12Kfp08wBmmWDgBZxhmWRpq7LjvY5ewBEVhdnTX4Z6PXKx3GIbkHAPa9S1Hm9eJJ45OQwgBogWtr1wVl3XiRqS30X3a9NF8poJxNBsgAXdYzehpcC+fgE+H4XFY0ODm6jNjTHSToBBRPRE9QERHs78LftoS0TVEdJiIjhHRLXnX/ysR7SeivUT0JyIypriOYVYIG1s8ui8/M4yR6fA5EU6kMR9LnXT9yFw0d7tR6W1w4cRMGEKIgrdPBmNo8hjDAWldU01uguxinJgJY22j+6QTIYYxEnpqwG8B8JAQYgOAh7KXT4KIzAC+CeBaAFsAvIOItmRv/k8hxHYhxNkAfgfgs5pEzTAMwzAFWMwJZdQfhcdhMbScpqfBjWAshdlwouDtU8G47g4oMhtaatA/HV7St7xvKszyE8bQ6JmAXw/gtuzftwH4iwLbnA/gmBCiTwiRAHBH9n4QQsznbecGUPi0nWEYhmE0QPYClyveMiP+qKGr3wCwJjuJtZAOPBxPIZxIG0IDDkiNmMm0wMAimvVYMo3RQBRrGrlnhTEueibgLUKIMQDI/m4usE0HgKG8y8PZ6wAARPRFIhoC8C4sUQEnopuIaBcR7ZqamlIkeIZhGIbJJ2fnN3tyYjjijxk+Ae/JWj8WckKZNogFoYw8zObYIjKUgZkIhIBuHuAMUwyqJuBE9CARvVTg5/piH6LAdblKtxDin4QQXQB+BuCjiz2IEOJWIcROIcTOpqam0v4JhmEYhimCercNXqcVx6dObhAc9UcN64Ai01XngolQsBHTKFMwZWTbzsWsCA+NSwvk7K/PGBlVXVCEEFctdhsRTRBRmxBijIjaAEwW2GwYQFfe5U4AowW2+z8AvwfwuUriZRiGYZhyISKsa3KjLy8BD8VTCESThnZAAbJWhHWFrQiNMgVTxmWzoLPOiaOLOKHsGwrAYTVhYwsn4Ixx0VOCci+AG7N/3wjgNwW2eR7ABiJaQ0Q2ADdk7wci2pC33RsAHFIxVoZhGIZZlnVNNTie5wUuN2QaXYICSJNZTxSQoBhlCmY+G5prFk/Ah/04s8MLi5lnDTLGRc9355cBXE1ERwFcnb0MImonovsAQAiRgiQt+SOAgwDuFEIckO+flbPsB/BqAB/T+h9gGIZhmHzWNddgKhhHIJoEsNCQafQKOCAl4P3Tp1sRTgXjMJsI9Qby1N7Q4kHfVAjpzMmxJtMZvDQSwFmdPn0CY5gi0W0QjxBiBsCVBa4fBXBd3uX7ANxXYLs3qxogwzAMw5TI2qz1Xd9UCOd012EkWwHvNLgGHAB6GlwIxlLwR5Koy0u2p4JxNLhtMJuM46m9vrkG8VQGw3ORXAMpABweDyKeyuCsLp9+wTFMEfD6DMMwDMMoxLpmSXcsj6Qf8UdhNZNh9NNLIftm958iQzHKGPp81mef51MbMfcN+wEAZ3MCzhgcTsAZhmEYRiG6612wmCjnhDLqj6LV64DJQNXjxVjMinDSyAn4KTrwfUN+1LttVbHiwKxuOAFnGIZhGIWwmk3obnDlEvCROeMP4ZHpqnfCRED/9MlOKEaagilT67CitdZx2kj6fUMBnNXp5RH0jOHhBJxhGIZhFGRdU01OgjLqj1ZFAyYA2C1mtPucJ1XAMxmB6ZDxKuCANJL+2OTJlo9HJoOs/2aqAk7AGYZhGEZB1jXV4MRMGLFkGuPzMXRWSQIOyFaECxVwfzSJVEYYMgFf3ywl4JmsE8pLIwEIAU7AmaqAE3CGYRiGUZB1TW4k0wK7TswhI6rDglCmt9F10jRMo03BzGdzay0iiTQePTIFANg75AcAtiBkqgJOwBmGYRhGQdZmR6A/flRKDI0+hj6f3gY3AtEk/JEEgIUEvNnj0DOsgrzh7HZsavXgE7/ch4n5GPYN+dFd7zKUXznDLAYn4AzDMAyjIOuaJDeRx45OA6iuCrjshNKfrYJPhWIAjFkBd1jN+N93noNIIo2P3fEC9g75WX7CVA2cgDMMwzCMgvhcNjTW2HBwbB5AdYyhl9nWUQsi4JHDUvXeyBIUAFjf7MHnr9+KZ/pmMRaI4axOr94hMUxRcALOMAzDMAojy1Aa3DY4rGadoymeNq8Tl2xowi93DSGdEZgKxuG0muG2Gfd/eOu5nXjjOR0AgHO663SOhmGKgxNwhmEYhlEYWYZSTfpvmRvO68JoIIbHjk7lpmAa2VebiPDvbzwT33n3Duzo9ukdDsMUhUXvABiGYRhmpbEuWwFv91ZfAn7V5hbUu22447lBhOIpw8pP8nHazLhmW5veYTBM0XAFnGEYhmEURk7Aq7ECbrOY8OYdHXjo4CSOTIQMNwWTYVYCnIAzDMMwjMKsb5YS8M4qTMAB4O3ndSOV1YBXQwWcYaoNTsAZhmEYRmG66l249T3n4i3nduodSlmsb67Beb1SQ2MzJ+AMozicgDMMwzCMCrx6ays8DqveYZTN28/rBgA0cgLOMIrDTZgMwzAMw5zG67a34ehEEFduatY7FIZZcehWASeieiJ6gIiOZn8XNO8komuI6DARHSOiWwrc/gkiEkTUqH7UDMMwDLM6cFjN+PR1m9Fca7wx9AxT7egpQbkFwENCiA0AHspePgkiMgP4JoBrAWwB8A4i2pJ3exeAqwEMahIxwzAMwzAMw1SIngn49QBuy/59G4C/KLDN+QCOCSH6hBAJAHdk7yfz3wA+BUCoGCfDMAzDMAzDKIaeCXiLEGIMALK/C4nMOgAM5V0ezl4HInoDgBEhxD61A2UYhmEYhmEYpVC1CZOIHgTQWuCmfyr2IQpcJ4jIlX2MVxcZx00AbgKA7u7uInfNMAzDMAzDMMqjagIuhLhqsduIaIKI2oQQY0TUBmCywGbDALryLncCGAWwDsAaAPuISL5+DxGdL4QYLxDHrQBuBYCdO3eyXIVhGIZhGIbRDT0lKPcCuDH7940AflNgm+cBbCCiNURkA3ADgHuFEC8KIZqFEL1CiF5IifqOQsk3wzAMwzAMwxgJPRPwLwO4moiOQnIy+TIAEFE7Ed0HAEKIFICPAvgjgIMA7hRCHNApXoZhGIZhGIapGN0G8QghZgBcWeD6UQDX5V2+D8B9yzxWr9LxMQzDMAzDMIwakBCrSxJNRFMABnTYdSOAaR32yyzAr4H+8GugP/wa6A+/BvrDr4H+rIbXoEcI0VTohlWXgOsFEe0SQuzUO47VDL8G+sOvgf7wa6A//BroD78G+rPaXwM9NeAMwzAMwzAMs+rgBJxhGIZhGIZhNIQTcO24Ve8AGH4NDAC/BvrDr4H+8GugP/wa6M+qfg1YA84wDMMwDMMwGsIVcIZhGIZhGIbREE7AGYZhGIZhGEZDOAFXGCK6hogOE9ExIrqlwO1ERF/P3r6fiHboEedKpojX4HIiChDR3uzPZ/WIc6VCRD8kokkiemmR2/kYUJkiXgM+BlSGiLqI6GEiOkhEB4joYwW24WNBJYp8/vk4UBEichDRc0S0L/safL7ANqv2GNBtEuZKhIjMAL4J4GoAwwCeJ6J7hRAv5212LYAN2Z8LAHw7+5tRgCJfAwB4XAjxOs0DXB38GMD/AvjJIrfzMaA+P8bSrwHAx4DapAD8vRBiDxF5AOwmogf4+0Azinn+AT4O1CQO4AohRIiIrACeIKL7hRDP5G2zao8BroAry/kAjgkh+oQQCQB3ALj+lG2uB/ATIfEMAB8RtWkd6AqmmNeAUREhxGMAZpfYhI8BlSniNWBURggxJoTYk/07COAggI5TNuNjQSWKfP4ZFcm+r0PZi9bsz6nOH6v2GOAEXFk6AAzlXR7G6Qd8Mdsw5VPs83tRdlnsfiLaqk1oTBY+BowBHwMaQUS9AM4B8OwpN/GxoAFLPP8AHweqQkRmItoLYBLAA0IIPgaysARFWajAdaee7RWzDVM+xTy/ewD0ZJfFrgNwD6TlL0Yb+BjQHz4GNIKIagDcBeDvhBDzp95c4C58LCjIMs8/HwcqI4RIAzibiHwA7iaibUKI/N6UVXsMcAVcWYYBdOVd7gQwWsY2TPks+/wKIeblZTEhxH0ArETUqF2Iqx4+BnSGjwFtyOpe7wLwMyHErwtswseCiiz3/PNxoB1CCD+ARwBcc8pNq/YY4ARcWZ4HsIGI1hCRDcANAO49ZZt7Abw32/l7IYCAEGJM60BXMMu+BkTUSkSU/ft8SMfBjOaRrl74GNAZPgbUJ/v8/gDAQSHEVxfZjI8FlSjm+efjQF2IqClb+QYROQFcBeDQKZut2mOAJSgKIoRIEdFHAfwRgBnAD4UQB4jo5uzt3wFwH4DrABwDEAHwfr3iXYkU+Rq8BcCHiCgFIArgBsEjYRWDiH4O4HIAjUQ0DOBzkJpv+BjQiCJeAz4G1OdiAO8B8GJWAwsA/wigG+BjQQOKef75OFCXNgC3Zd3JTADuFEL8jnMiCR5FzzAMwzAMwzAawhIUhmEYhmEYhtEQTsAZhmEYhmEYRkM4AWcYhmEYhmEYDeEEnGEYhmEYhmE0hBNwhmEYhmEYhtEQTsAZhmEYhmEYRkM4AWcYhmEYhmEYDeEEnGEYhikIEZ1HRPuJyEFEbiI6QETb9I6LYRim2uFBPAzDMMyiENG/AXAAcAIYFkJ8SeeQGIZhqh5OwBmGYZhFISIbgOcBxAC8QgiR1jkkhmGYqoclKAzDMMxS1AOoAeCBVAlnGIZhKoQr4AzDMMyiENG9AO4AsAZAmxDiozqHxDAMU/VY9A6AYRiGMSZE9F4AKSHE/xGRGcBTRHSFEOLPesfGMAxTzXAFnGEYhmEYhmE0hDXgDMMwDMMwDKMhnIAzDMMwDMMwjIZwAs4wDMMwDMMwGsIJOMMwDMMwDMNoCCfgDMMwDMMwDKMhnIAzDMMwDMMwjIZwAs4wDMMwDMMwGvL/AzsQBtXIQhxmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,u_pred[0,:,0],label='u_pred')\n",
    "plt.plot(x_domain,U(x_domain),label='truth')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('u_pred')\n",
    "plt.title('u-prediction')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,U(x_domain),label='truth')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('dudx_pred')\n",
    "plt.title('dudx-prediction')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,u_pred[0,:,0]-U(x_domain),label='error')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('d2udx2_pred')\n",
    "plt.title('d2udx2-prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8466b730",
   "metadata": {},
   "source": [
    "# 7. Save the Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "262c56f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "picn_process_data = {'x_domain':x_domain,\n",
    "                     'epoch_number_list':np.asarray(epoch_number_list),\n",
    "                     'total_loss_list':np.asarray(total_loss_list),\n",
    "                     'domain_loss_list':np.asarray(domain_loss_list),\n",
    "                     'boundary_loss_list':np.asarray(boundary_loss_list),\n",
    "                     'ux_list':np.vstack(ux_list),\n",
    "                     'dudx_list':np.vstack(dudx_list),\n",
    "                     'd2udx2_list':np.vstack(d2udx2_list)}\n",
    "savemat('picn_process_data.mat', picn_process_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8958426",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
